This compilation of finance and accounting research papers examines various topics. Several chapters analyze equity markets, focusing on market anomalies and their robustness, intraday volume-volatility relationships, and winner-loser effects. Other chapters explore options and futures pricing, along with portfolio diversification strategies using quadratic programming. Furthermore, the collection investigates corporate finance issues, including debt management, earnings management, and the impact of capital structure on firm value. Finally, some papers address methodological issues such as sample selection bias and robustness testing in empirical financial research.

Quantitative Finance and Accounting: A Study Guide

Advances in Quantitative Analysis of Finance and Accounting: A Study Guide

Key Concepts Review:

This study guide covers a range of topics within quantitative finance and accounting, including:

• Corporate Finance: Hedging strategies using futures and options, the impact of collateral constraints on debt management and investment incentives, the pricing of initial public offerings (IPOs), and capital structure decisions.
• Investments: Momentum and mean reversion in stock markets, portfolio optimization using the single-index model (SIM).
• Financial Accounting: The value relevance of earnings, particularly for high-tech firms, earnings management in corporate voting, and the accruals anomaly.
• Quantitative Methods: Linear and quadratic programming in marketing, variance ratio tests, Markov chains, and econometric techniques like 2SLS.

Short Answer Quiz:

1. Explain the role of futures and straddles in hedging price risk. How does the optimal futures position relate to the price of the underlying asset?
2. Describe the collateral constraint in the context of debt management. How does the use of straddles impact the financial resources available for investment?
3. Explain the benchmark case in the collateral constraint model where the price is known with certainty. How is the optimal level of capital determined in this scenario?
4. Briefly explain the concept of IPO underpricing. Why might underwriters intentionally underprice an IPO?
5. What are the key factors influencing the uncertainty of IPO price according to Liu, Wu, and Chen (2008)?
6. How do Lee, Press, and Choi (2008) classify high-tech and low-tech firms? What are their primary findings regarding the value relevance of earnings for these two groups?
7. Describe the accruals anomaly. How does it relate to earnings management and corporate voting?
8. Explain the difference between the full covariance model (FCM) and the single index model (SIM) in portfolio optimization. What is the key assumption of SIM and its primary advantage?
9. What are the determinants of winner-loser effects in national stock markets as discussed by Pan (2008)?
10. How do Jog and Zhu (2008) analyze stock splits, reverse stock splits, and stock dividends? What are their primary findings regarding the market reaction to these events?

Answer Key:

1. Futures contracts allow firms to lock in a future price for an asset, mitigating the risk of unfavorable price movements. Straddles, consisting of both put and call options, provide a wider range of price protection. The optimal futures position is related to the partial derivative of the profit function with respect to the price.
2. The collateral constraint requires that the value of the borrower’s assets be sufficient to cover the debt obligations in all possible price scenarios. Shorting straddles can increase initial investment funds, but at the cost of reduced resources later. Conversely, buying straddles has the opposite effect.
3. In the benchmark case with certainty, the problem simplifies to profit maximization with respect to the investment level. The optimal capital level is determined by setting the collateral constraint to zero. This means the firm invests as much as possible given its debt obligations.
4. IPO underpricing refers to the phenomenon where the initial offering price of a stock is set below its market value, resulting in immediate gains for initial investors. Underwriters might underprice to ensure the successful distribution of shares, reduce their own risk, or create positive initial buzz around the IPO.
5. The uncertainty of IPO price is driven by underwriters’ imperfect information, gathered from a potentially biased customer pool, and unexpected events occurring between price setting and trading commencement.
6. Lee, Press, and Choi (2008) employ several methods, including SIC codes and R&D intensity, to distinguish between high-tech and low-tech firms. They find that earnings are less value-relevant for high-tech firms, potentially due to higher information asymmetry and growth option value.
7. The accruals anomaly suggests that stocks of companies with high accruals tend to underperform those with low accruals. This could be related to earnings management, as managers might manipulate accruals to influence short-term performance around corporate voting events.
8. FCM considers the full covariance matrix between all assets in a portfolio, while SIM simplifies this by assuming that asset returns are driven by a single common factor. SIM’s key assumption is the zero cross-sectional correlation of residuals, which reduces computational complexity.
9. Pan (2008) finds that momentum strategies can be profitable in some national stock markets, with returns influenced by factors such as horizon, currency, and the presence of mean reversion. Variance ratio tests suggest deviations from random walks in several markets, although often not statistically significant.
10. Jog and Zhu (2008) examine market reactions to stock splits, reverse splits, and stock dividends using event study methodology. They find that the market reacts positively to splits and dividends but negatively to reverse splits in the short term. They also analyze changes in trading volume and corporate governance characteristics around these events.

Essay Questions:

1. Critically evaluate the role of options and futures contracts in managing price risk, drawing on the models presented in Agliardi and Andergassen (2008).
2. Discuss the implications of the findings of Lee, Press, and Choi (2008) for financial reporting and valuation practices, particularly in the high-tech sector.
3. Analyze the determinants of IPO underpricing, incorporating the insights from Liu, Wu, and Chen (2008).
4. Discuss the merits and limitations of the single index model (SIM) in portfolio optimization, comparing it to the full covariance model (FCM).
5. Critically examine the evidence presented by Pan (2008) on momentum and mean reversion in international stock markets.

Glossary of Key Terms:

• Accruals Anomaly: An empirical observation that stocks of firms with high accruals tend to underperform those with low accruals.
• Collateral Constraint: A restriction on borrowing that requires the borrower’s assets to be sufficient to cover the debt in all possible scenarios.
• Earnings Management: The use of accounting techniques to manipulate reported earnings.
• Futures Contract: An agreement to buy or sell an asset at a specified price on a future date.
• Gross Spread: The difference between the price at which an underwriter buys shares from the issuing company in an IPO and the price at which they are sold to the public.
• Hedging: A strategy designed to reduce investment risk.
• IPO Underpricing: The setting of an IPO offer price below the market value of the shares.
• Markov Chain: A stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
• Mean Reversion: The tendency of a variable to return to its long-term average after periods of deviation.
• Momentum: The tendency for rising asset prices to continue rising and falling asset prices to continue falling.
• Options Contract: A contract giving the holder the right, but not the obligation, to buy or sell an asset at a specified price on or before a certain date.
• Single Index Model (SIM): A portfolio optimization model that assumes asset returns are driven by a single common factor.
• Straddle: An options strategy involving the simultaneous purchase or sale of both a call and a put option with the same strike price and expiration date.
• Variance Ratio Test: A statistical test used to determine whether a time series exhibits mean reversion or mean aversion.
• Value Relevance: The extent to which accounting information is reflected in stock prices.

Quantitative Analysis in Finance and Accounting

Briefing Doc: Advances in Quantitative Analysis of Finance and Accounting

This briefing doc reviews key themes and findings from a collection of excerpts from the book “Advances in Quantitative Analysis of Finance and Accounting (Advances in Quantitative Analysis of Finance and Accounting) Volume 6”. The excerpts cover diverse topics, including:

• Hedging Strategies in Emerging Markets: This section focuses on how emerging economies can utilize hedging instruments, like futures and straddles, to mitigate financial constraints and incentivize investments in the face of price uncertainty.
• Key Insight: Short positions in futures and straddles can provide additional financial resources for investment in the initial period, potentially improving capital allocation in emerging economies.
• Quote: “Since in the present model the economy has no initial endowments, for s > 0 straddles are used for financing purposes since shortening straddles reduces financial constraints in the first period where investment decisions have to be taken.”
• Applications of Quadratic Programming in Marketing Strategy: The excerpt explores the application of quadratic programming to optimize marketing strategies in situations with decreasing unit profit functions.
• Key Insight: Quadratic programming models allow for a more nuanced approach to marketing strategy, factoring in diminishing returns and constraints related to advertising budget, sales force, and production capacity.
• Quote: “First, with the assumption of a decreasing unit profit function, the number of markets penetrated or the distribution channels employed (xi > 0) in the optimum solution will be less than that of LPMS model.”
• Value Relevance of Earnings for High-Tech Firms: This section delves into the relationship between stock returns and accounting earnings, specifically for high-tech companies. It highlights the challenge of expense mismatching and the impact of noise on interpreting earnings data.
• Key Insight: Standard accounting metrics may not fully capture the economic performance of high-tech firms, especially considering the long-term impact of R&D investments and the prevalence of noise in reported earnings.
• Quote: “When the change in accounting earnings is adopted as a proxy for unexpected earnings, Xt is garbled with components that are not incorporated in stock returns.”
• Impact of Stock Splits and Stock Dividends on Shareholder Value: This excerpt examines the market’s reaction to stock splits, reverse stock splits, and stock dividends.
• Key Insight: Stock splits tend to generate positive market reactions, while reverse stock splits receive negative reactions, suggesting a potential behavioral aspect to investor perceptions.
• Quote: “The results show that the market seems to react positively to stock splits in the time period immediately around the event month, but continues to react negatively to reverse splits in the short term.”
• Intraday Volatility and Trading Volume Relationship in the Dow Jones Industrial Average (DJIA): This section explores the causal relationship between stock return volatility and trading volume using intraday data from the DJIA.
• Key Insight: The study reveals a bi-directional causal link between volatility and volume, highlighting the complex interplay of these factors in influencing intraday stock market behavior.
• Option Approach to Pricing Initial Public Offerings (IPOs): This excerpt proposes a novel model for pricing IPOs based on options theory, considering factors like underwriter risk tolerance and price uncertainty.
• Key Insight: The model explains underpricing and underwriter spreads in IPOs as a function of risk aversion and uncertainty inherent in the process, providing a theoretical framework for understanding these market phenomena.
• Quote: “In this chapter, an IPO pricing model consistent with rational economic theory has been proposed to explain the underpricing and underwriters’ spreads. The model takes into account the uncertainty in the price of the new issue and the underwriter’s risk tolerance.”
• Momentum and Mean Reversion in National Stock Markets: This section investigates momentum and mean-reversion patterns in international stock markets, highlighting the role of these phenomena in explaining market dynamics.
• Key Insight: The analysis suggests a significant winner-loser effect across national stock markets, with momentum strategies potentially yielding profits in the short term.
• Quote: “The results show that the buy-winners-and-sell-losers trading rule is profitable across the 16 national stock market indexes. The profits are statistically significant for the 6- and 12- month horizons in local currency and the 6-month horizon in US dollar.”
• Impact of Australia’s Dividend Imputation Tax System on Firm Value: This excerpt examines the impact of Australia’s dividend imputation tax system on firm value and capital structure decisions.
• Key Insight: The study suggests that the imputation tax system may influence firm value and capital structure choices, highlighting the importance of considering tax policy implications in corporate finance decisions.
• Momentum and Mean Reversion in Nikkei Index Futures: This section analyzes intraday data from the Nikkei index futures market to understand momentum and mean reversion patterns, which impact trading strategies.
• Key Insight: The findings reveal that momentum and mean reversion dynamics are present in the Nikkei futures market and can vary across different time horizons and intraday periods.

This briefing doc provides a summary of the main themes and insights from the selected excerpts. The diverse topics covered showcase the breadth of applications of quantitative methods in understanding financial markets, corporate finance, and marketing strategies. Further analysis and interpretation of these findings would be needed to inform specific investment decisions or business strategies.

Financial Markets and Corporate Finance: Key Themes and Research

FAQ: Main Themes and Ideas from Finance and Accounting Literature

1. How does price uncertainty and the use of financial instruments like futures and straddles affect investment decisions in emerging markets?

Emerging markets often face price volatility, impacting investment decisions. Financial instruments like futures and straddles can be used to hedge against this risk. Shorting futures contracts can increase available funds for investment in the present while locking in a future price for the output. Straddles can serve both financing and speculative purposes. Shorting straddles can provide additional funding for investments, while longing straddles can alleviate financial constraints in the future when repayments are due. The optimal hedging strategy depends on factors like the cost of default and the level of price uncertainty.

2. How can a concave quadratic programming marketing strategy (QPMS) model be used to optimize marketing efforts across different markets?

The QPMS model helps businesses allocate marketing resources efficiently. It considers factors like unit profit, advertising costs, sales force efforts, and capacity constraints to maximize profit. By incorporating a decreasing unit profit function, the model realistically reflects market saturation. It provides insights into the optimal number of markets to penetrate and the ideal distribution channels to employ. The model also offers valuable sensitivity analysis, enabling businesses to understand the impact of changes in market conditions on their optimal strategy.

3. Why is the value relevance of earnings for high-tech firms different from traditional firms?

Traditional valuation models may not accurately capture the value of high-tech firms due to factors like significant R&D investments, intangible assets, and rapid technological advancements. High-tech firms often incur substantial R&D expenses that are expensed rather than capitalized, leading to lower reported earnings in the short term. Moreover, the rapid evolution of technology can result in shorter product lifecycles and increased uncertainty about future cash flows. This makes it challenging to accurately predict the future earnings potential of high-tech firms.

4. What role does corporate governance play in a company’s decision to implement anti-takeover provisions?

Anti-takeover provisions, like supermajority voting requirements or staggered boards, can impact a company’s vulnerability to acquisitions. Research suggests that companies with weaker corporate governance structures are more likely to adopt these provisions. This could be because these provisions serve to entrench existing management and protect them from hostile takeovers, even if such a takeover might be beneficial to shareholders.

5. How does stock split and reverse stock split affect market value and liquidity?

While often perceived as signaling events, stock splits and reverse splits can have differing impacts on market value and liquidity. Stock splits tend to have a positive short-term effect on market value, likely due to increased accessibility for smaller investors. Reverse stock splits, on the other hand, often result in negative short-term reactions, possibly due to associations with financial distress. However, long-term impacts on market value are less clear. Stock splits generally lead to increased trading volume and liquidity, as the lower price attracts a wider range of investors. Conversely, reverse stock splits can decrease liquidity as the higher price per share limits the potential pool of buyers.

6. How can Markov chains be used to analyze intraday momentum and mean reversion in the Nikkei Index Futures market?

Markov chains offer a powerful tool to study the dynamic behavior of financial markets. By modeling the transition probabilities between rising and falling returns, researchers can identify patterns of momentum and mean reversion. Analysis of the Nikkei Index Futures market suggests significant momentum at shorter intervals like one minute. However, at longer intervals like 10 or 20 minutes, a pattern of mean reversion emerges. This finding implies that short-term price trends tend to continue, while longer-term trends are more likely to reverse.

7. What factors influence the underpricing of initial public offerings (IPOs) from an options pricing perspective?

Underpricing in IPOs can be viewed as the underwriter selling a put option to the issuer, guaranteeing a minimum price for the shares. The level of underpricing is influenced by factors that impact the value of this implicit put option. Higher price uncertainty, arising from imperfect information or market volatility, increases the value of the put and leads to greater underpricing. Additionally, the underwriter’s risk tolerance plays a role. Underwriters willing to absorb larger potential losses can offer lower underpricing and higher gross spreads.

8. How does the dividend imputation tax system in Australia affect firm value and the cost of capital?

Australia’s dividend imputation system aims to reduce the double taxation of dividends. This system can impact firm value and the cost of capital. By providing a tax credit to shareholders for the corporate tax already paid on dividends, the system can reduce the effective tax burden on equity income. This, in turn, can lower the cost of equity capital for firms, potentially leading to higher firm valuations. However, the actual impact of the dividend imputation system on firm value is complex and depends on various factors, including the firm’s dividend policy, the marginal tax rates of shareholders, and the availability of other tax shields.

Financial Models and Key Researchers

Timeline of Main Events

This information is insufficient to construct a timeline of events. The provided text excerpts discuss various financial and economic concepts, models, and analyses but lack any specific dates or chronological order of events.

Cast of Characters

Due to the nature of the provided source material, pinpointing specific individuals with biographical details is difficult. However, we can identify some key figures whose work is referenced or whose models are discussed:

1. E. Agliardi & R. Andergassen: – Contribution: Authors of a study focusing on the relationship between collateral constraints, debt management, and investment incentives. – Specifics: They develop a model where firms use futures and straddles to hedge risk associated with price uncertainty. – Source: “010-Advances In Quantitative Analysis Of Finance And Accounting (Advances in Quantitative Analysis of Finance and Accounting) Volume 6 ( PDFDrive ).pdf”

2. Moschini and Laplan: – Contribution: Researchers cited for their work on the role of futures and options in hedging price risk. – Specifics: Their work supports the idea that these financial instruments can mitigate uncertainty in markets with fluctuating prices. – Source: Mentioned within “010-Advances In Quantitative Analysis Of Finance And Accounting”

3. Shleifer and Vishny: – Contribution: Authors whose work explores alternative objectives for firms beyond profit maximization. – Source: Cited as a footnote in “A Concave Quadratic Programming Marketing Strategy Model”.

4. Navarro, Winn and Shoenhair, and Boudreaux and Holcombe: – Contribution: Also cited for their research on firm objectives that go beyond maximizing profits. – Source: Grouped with Shleifer and Vishny in the footnote.

5. Luenberger: – Contribution: Author of a work likely on optimization techniques, referenced for the concept of Lagrangian multipliers in mathematical programming. – Source: Mentioned in the section discussing the QPMS model.

6. B. B. Lee, E. Press & B. B. Choi: – Contribution: Researchers who investigate the value relevance of earnings for high-tech firms. – Specifics: They analyze the relationship between stock returns and accounting data, taking into account factors like expense mismatching and R&D intensity. – Source: Authors of “The Value Relevance of Earnings for High-Tech Firms”.

7. Francis and Schipper: – Contribution: Authors of a study providing a method for classifying high-tech and low-tech firms using Standard Industrial Classification (SIC) codes. – Source: Referred to in the analysis of high-tech firms.

8. Lo and MacKinlay: – Contribution: Developers of a variance ratio test for assessing serial correlation in financial time series data. – Specifics: Their test helps determine if a stock’s price movements exhibit patterns beyond random fluctuations. – Source: Used to analyze national stock market indexes in “Determinants of Winner–Loser Effects in National Stock Markets”.

9. Jones: – Contribution: Developed a method for estimating abnormal accruals in accounting data. – Specifics: This method is likely used to analyze earnings management practices. – Source: Cited in a table analyzing abnormal accruals surrounding shareholder votes.

10. Kothari et al.: – Contribution: Authors of a study that proposes a refinement or alternative to Jones’ method for estimating abnormal accruals. – Source: Mentioned alongside Jones.

11. A. F. Darrat, S. Rahman & M. Zhong: – Contribution: Researchers examining the intraday volume-volatility relationship in Dow Jones Industrial Average (DJIA) stocks. – Specifics: They analyze high-frequency data to understand how trading volume and price fluctuations interact throughout the trading day. – Source: “Intraday Volume-Volatility Relation of the DOW”.

12. S. Liu, C. Wu & P. H. Chen: – Contribution: Proponents of an option pricing approach to understanding initial public offerings (IPOs). – Specifics: They develop a model that incorporates underwriter risk tolerance and market uncertainty to explain IPO pricing dynamics. – Source: “The Pricing of Initial Public Offerings: An Option Approach”.

13. M.-S. Pan: – Contribution: Investigates momentum and mean reversion strategies in national stock markets using an international perspective. – Specifics: Analyzes stock market index returns to determine if patterns of continuations or reversals exist and how these relate to profitability. – Source: “The Momentum and Mean Reversion of Nikkei Index Futures”.

14. Sharpe: – Contribution: Developed the Single Index Model (SIM), a simplified model for portfolio selection. – Specifics: SIM assumes that stock returns are primarily driven by a single common factor, typically a broad market index. – Source: Heavily discussed in “Deterministic Portfolio Selection Models”.

15. Miller: – Contribution: Known for his work on the impact of taxes on capital structure. – Specifics: Extended the Modigliani-Miller theorem to include the effects of personal income tax. – Source: Cited in the discussion of capital structure and dividend imputation tax in Australia.

16. Modigliani and Miller: – Contribution: Famous for their groundbreaking work on capital structure irrelevance, suggesting that in perfect markets, a firm’s value is independent of its debt-equity mix. – Specifics: Their theory forms the foundation for much of modern corporate finance research on capital structure. – Source: Fundamental to the discussion of capital structure in the Australian context.

17. DeAngelo and Masulis, and Dammon and Senbet: – Contribution: Economists who investigated the role of non-debt tax shields (like depreciation allowances) in corporate financing decisions. – Source: Cited in the section explaining non-debt tax shield theories.

Please note that the information provided in the excerpts only allows for a limited understanding of these figures’ work. More comprehensive biographical information would require consulting their individual publications and other sources.

Equity Market Anomalies and Behavior

Three chapters in Volume 6 of Advances in Quantitative Analysis of Finance and Accounting discuss equity markets: “Evaluating the Robustness of Market Anomaly Evidence,” “Intraday Volume–Volatility Relation of the DOW: A Behavioral Interpretation,” and “Determinants of Winner–Loser Effects in National Stock Markets.” [1]

• Evaluating the Robustness of Market Anomaly Evidence examines how sample selection and influential observations impact evidence of anomalous stock returns. [2] The analysis focuses on two purported anomalies: the accruals anomaly and the forecast-to-price anomaly. [3] The chapter analyzes the impact of passive deletion on size-adjusted hedge returns, finding that passive deletion has a greater effect on returns related to the forecast-to-price strategy than on the accrual strategy. [4, 5] The chapter also examines the impact of extreme returns on size-adjusted hedge returns and finds that mean hedge returns decrease when less-extreme deciles and quintiles are used. [6, 7]
• Intraday Volume–Volatility Relation of the DOW: A Behavioral Interpretation uses behavioral insights to interpret empirical results of a study by Darrat et al., which found a positive causal effect from volume to volatility in intraday trading data from the 30 stocks of the Dow Jones Industrial Average (DJIA). [8, 9] This chapter argues that overconfidence in investors can explain a positive causal effect from volume to volatility. [10] The authors find that the Gibbons binomial pooled z-test statistic is highly significant, with a large, positive summed coefficient, supporting the hypothesis that higher trading volume leads to higher return volatility. [11]
• Determinants of Winner–Loser Effects in National Stock Markets examines the determinants of profits from momentum and contrarian strategies used on national stock market indexes. [12] The study analyzes monthly stock market index data from 16 countries between December 1969 and December 2000, finding that momentum strategies are profitable over horizons from 3 to 12 months, while contrarian strategies are profitable for longer horizons such as 2 years or longer. [13, 14] However, the profit is only statistically significant for the 6-month horizon. [14] The chapter concludes that most stock market indexes follow a mean-reverting process, meaning that there are positive autocorrelations in short-horizon returns and negative autocorrelations in long lags. [12, 15]

This volume also includes a chapter on the Canadian stock market, “Thirty Years of Canadian Evidence on Stock Splits, Reverse Stock Splits, and Stock Dividends.” [16, 17]

Portfolio Diversification Models Under Uncertainty

One chapter in Volume 6 of Advances in Quantitative Analysis of Finance and Accounting discusses portfolio diversification: “Deterministic Portfolio Selection Models, Selection Bias, and an Unlikely Hero.” This chapter examines how effectively different portfolio selection models diversify investments under conditions of generalized uncertainty.

The author, Herbert E. Phillips, reviews four common portfolio selection models:

• The Full Covariance Model (FCM)
• The Constant Correlation Model (CCM)
• Sharpe’s Single Index Model (SIM)
• The Single Index Analog (SIM)*

Phillips analyzes the models’ diversification strategies, finding that the models accomplish risk/return trade-offs in different ways. As the models attempt to diversify by accepting lower target rates of return in exchange for risk reduction, a systematic relationship emerges between portfolio target rate of return and the number of stocks included in the portfolios.

• For target monthly returns of 2.5% or less, the models ranked by portfolio size are CCM, FCM, SIM, and SIM.*
• For target monthly returns of 3% or less, the models ranked by portfolio size are FCM, SIM, and SIM.*

These results show that some models are better at identifying diversification opportunities than others. For example, CCM is better than FCM at identifying diversification opportunities for target returns of 2% or less. However, for target returns of 3% or less, FCM is better than SIM* at identifying such opportunities, and SIM* is better than SIM.

Phillips argues that the differences in the models’ diversification strategies stem from how the models incorporate covariance or correlation information.

• FCM diversifies by seeking out securities with less than perfect correlation, using sample estimates of portfolio mean and portfolio variance to make investment decisions.
• CCM uses the average of all pairwise correlations from the sample correlation matrix to estimate a constant correlation coefficient, then substitutes that single value for all off-diagonal elements in the sample correlation matrix. Phillips notes that there is no statistical justification for using a single average to represent all pairwise correlations and that this practice is prone to error.
• SIM is on the opposite extreme from FCM. It eliminates all covariance effects, diversifying solely through the law of large numbers. As a result, SIM’s portfolios tend to include a larger number of stocks than the portfolios of other models.
• SIM*, like SIM, uses a single index framework but does not force diagonalization of the variance–covariance matrix.

Phillips concludes that, under conditions of generalized uncertainty, SIM is the model least susceptible to estimation error because it does not rely on sample estimates of covariance or correlation. This conclusion is unexpected, as SIM is generally viewed as a simplified version of FCM and therefore potentially less accurate.

Earnings Management and Antitakeover Charter Amendments

One chapter in Volume 6 of Advances in Quantitative Analysis of Finance and Accounting discusses earnings management: “Earnings Management in Corporate Voting: Evidence from Antitakeover Charter Amendments.” This chapter examines whether managers manipulate earnings around the time of antitakeover charter amendment (ATCA) proposals.

The authors hypothesize that:

• Managers will accelerate the recognition of income-increasing accruals prior to a shareholder vote.
• Managers will postpone the recognition of income-decreasing accruals until after a shareholder vote.

To test their hypotheses, the authors analyze a sample of 148 firms that proposed ATCAs between 1988 and 1997. They use a performance-matched discretionary accrual measure that adjusts for earnings momentum and mean reversion in earnings.

The study finds that firms proposing ATCAs have weak but statistically significant negative abnormal accruals in the proposal year. These results appear to be driven by firms proposing restrictive amendments such as classified board and supermajority amendments, which have strong negative abnormal accruals in the proposal year. The authors interpret this finding as evidence that managers of firms proposing restrictive amendments manage earnings opportunistically by deferring the recognition of negative accruals until after the shareholder vote.

The chapter also discusses prior research on earnings management in corporate voting:

• DeAngelo (1988) finds that incumbent executives inflate earnings during a proxy contest using abnormal accruals.
• Perry and Williams (1994) criticize DeAngelo’s measure of earnings management, arguing that it may include nondiscretionary components.
• Kothari et al. (2005) find that discretionary accrual measures that do not adjust for a performance-matched firm’s discretionary accruals are unreliable.

This chapter contributes to the literature by:

• Providing additional evidence of managerial influence in corporate voting.
• Adding to the literature that examines earnings management in episodic corporate events.
• Using a more robust methodology to detect earnings management in a less extreme corporate voting event than a proxy contest.

The authors conclude that their findings suggest that managers of firms proposing restrictive ATCAs may manipulate earnings to influence shareholder votes.

Debt Management, Stock Splits, and Firm Value

Volume 6 of Advances in Quantitative Analysis of Finance and Accounting includes three chapters that discuss debt management: “Collateral Constraints, Debt Management, and Investment Incentives,” “Thirty Years of Canadian Evidence on Stock Splits, Reverse Stock Splits, and Stock Dividends,” and “Corporate Capital Structure and Firm Value: A Panel Data Evidence from Australia’s Dividend Imputation Tax System.”

• “Collateral Constraints, Debt Management, and Investment Incentives” analyzes how emerging economies can use hedging to manage debt. The authors use a two-period model of sovereign debt that includes default risk and endogenous collateral. They find that, in addition to futures, optimality requires either concave or convex hedging, depending on the cost of default.
• If the cost of default is high, optimality requires a short position in straddles, and the economy is induced to never default.
• If the cost of default is low, optimality requires a long position in straddles, and the economy is induced to default with a probability greater than 50%.
• “Thirty Years of Canadian Evidence on Stock Splits, Reverse Stock Splits, and Stock Dividends” examines trends in stock splits, reverse stock splits, and stock dividends in Canada between 1970 and 2002. The authors investigate several hypotheses about why firms might engage in these activities, including signaling, optimal price range, and valuation hypotheses. They also analyze whether stock splits allow firms to change their shareholder composition to reduce monitoring by large shareholders. Their findings are inconclusive, meaning that they are unable to determine why Canadian firms engage in stock splits, reverse stock splits, and stock dividends.
• “Corporate Capital Structure and Firm Value: A Panel Data Evidence from Australia’s Dividend Imputation Tax System” examines how financial leverage affects firm value in Australia. The author analyzes data from a sample of 45 Australian firms between 1988 and 1997. The study uses a model that controls for expected tax-adjusted earnings, growth potential, systematic risk, dividend payouts, and firm size. The author finds that firm value increases significantly with financial leverage. They also find a positive relationship between dividend payouts and both debt issuance and firm value. These results suggest that, although Australia’s dividend imputation tax system reduces the tax benefits of debt, corporate leverage still has a positive effect on firm value in Australia.

Hedging Sovereign Debt with Futures and Options

One chapter in Volume 6 of Advances in Quantitative Analysis of Finance and Accounting discusses hedging instruments: “Collateral Constraints, Debt Management, and Investment Incentives.” This chapter develops a model of how emerging economies can use futures and options to hedge against market risks.

The chapter notes that prior research has shown that:

• Futures provide a perfect hedge in a model of competitive firms with output price uncertainty where all input decisions are made before uncertainty is resolved [1].
• Options are a useful hedging tool when firms face nonhedgeable price risk, because the multiplicative nature of price and exchange rate risks creates hedging demand for instruments with nonlinear payoffs [2].
• Options can also be useful when a firm’s investment opportunities are correlated with the availability of funds [2].

This chapter extends prior research by analyzing hedging in the context of sovereign debt. The authors develop a two-period model of sovereign debt with default risk and endogenous collateral. The model assumes that the debtor country produces a tradable good with an uncertain price and a nontradable good with a certain price. The debtor country can use futures and straddles to hedge against the price uncertainty of the tradable good.

The authors find that:

• A short position in futures is optimal because it increases the funds available for investment in the first period. The optimal futures position is equal to the expected change in the value of the tradable good production due to a change in price. Additionally, the optimal futures position does not depend on the cost of default [3].
• The optimal hedging strategy also includes nonlinear hedging with options. Whether to take a short or long position in straddles depends on the cost of default:If the cost of default is high, the country should take a short position in straddles, which increases funds available for investment in the first period and reduces the probability of default [4].
• If the cost of default is low, the country should take a long position in straddles, which allows the country to speculate on the price of the tradable good, increasing the probability of default [4].

The chapter concludes that the optimal hedging strategy for an emerging economy depends on the country’s financial constraints and the cost of default.

A straddle involves simultaneously purchasing a call option and a put option on the same asset with the same strike price and expiration date.

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

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