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This study intends to examine the price-volume movements in the Arab stock markets, in order to determine the impact of changes in trade volume on the volatility of stock prices as expressed by the unified MAF stock price index. The research covers a sample of eight out of the fifteen Arab stock markets included in the Arab Monetary Fund database, using monthly data from 1994 to 2006. The study found that there is an increasing in both trading volume and stock price volatility, which may be considered as a recent phenomenon in the majority of the Arab stock markets. The study also found that the volume-stock price movements are significantly integrated for all selected markets, while the highest correlation coefficient between volume and stock price movement was found in Saudi stock market, Amman stock market, Muscat stock market and Kuwait stock market respectively. Finally, the correlation between volume and prices movement is higher in the stock markets of the oil Arab states compared to the non-oil Arab states.

A large body of academic research describes the optimal decisions that corporations should make, given certain assumptions and conditions. Anecdotal evidence, however, suggests that the way that corporations actually make decisions is not always consistent with the academic decision rules. In this paper, we analyze a comprehensive survey that describes the current practice of corporate finance. This allows us to identify areas where the theory and practice of corporate finance are consistent and areas where they are not.

We study the financial impact of the 2018-2019 U.S.-China trade war on firms engaged in global supply chains. Around the dates when higher tariffs were announced, U.S. firms depending more on exports to and imports from China experienced larger declines in market values. Guided by a model that identifies various direct and indirect trade channels through which tariffs affect firms’ profits, we examine the transmission of tariff shocks through firms’ suppliers and customers. We confirm our results by exploiting the within-firm variation in product exposure based on two tariff lists and a positive trade negotiation as a reverse experiment.

Financial market volatility is an important input for investment, option pricing and financial market regulation. In this review article, we compare the volatility forecasting findings in 93 papers published and written in the last two decades. This article is written for general readers in Economics, and its emphasis is on forecasting instead of modelling. We separate the literature into two main streams; the first consists of research papers that formulate volatility forecasts based on historical price information only, while the second includes research papers that make use of volatility implied in option prices. Provided in this paper as well are volatility definitions, insights into problematic issues of forecast evaluation, the effect of data frequency on volatility forecast accuracy, measurement of "actual" volatility, and the confounding effect of extreme values on volatility forecasting performance. We compare volatility forecasting results across different asset classes, and markets in different geographical regions. Suggestions are made for future research.

This study documents the significant profitability of “time-series momentum” strategies in individual stocks in the US markets from 1927 to 2014 and in international markets since 1975. Unlike cross-sectional momentum, time-series stock momentum performs well following both up- and down-market states, and it does not suffer from January losses and market crashes. An easily formed dual-momentum strategy, combining time-series and cross-sectional momentum, generates striking returns of 1.88% per month. We test both risk based and behavioral models for the existence and durability of time-series momentum and suggest the latter offers unique insights into its continuing factor dominance.

Every structure has natural frequencies. Minor shocks in these frequencies can bring down any structure, e.g. a bridge. An Investment Universe also has natural frequencies, characterized by its eigenvectors. A concentration of risks in the direction of any such eigenvector exposes a portfolio to the possibility of greater than expected losses (indeed, maximum risk for that portfolio size), even if that portfolio is below the risk limits. This is particularly dangerous in a risk-on/risk-off regime. Managing Risk is not only about limiting its amount, but also controlling how this amount is concentrated around the natural frequencies of the investment universe.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.

The COVID-19 pandemic has had unexpected consequences for many industries. One of these industries is the video game industry. People increased their time at home, and they turned to play games to socialize and get away from stress. In this study, we aim to examine the effects of COVID-19 on the game industry. In the first part of our study, we discussed the social and psychological effects of games on people during the pandemic period. The share values of essential companies in the gaming industry such as Electronic Arts, Activision-Blizzard, Ubisoft, Capcom, and Take-Two and the number of daily active users and active players of Steam, the digital game platform with millions of users, were examined. We observed that there is a significant increase in the number of active players and users daily. The hypothesis that large companies in the gaming industry increased their share values were established, and this hypothesis was confirmed as a result of the hypothesis tests. We examined the pandemic period, the sector's growth, and the economic dimension of this increase. Due to the increase in the demand for video games, game companies' stock prices are also increasing.

This study develops an accounting-based valuation model in a setting where firms have the flexibility to expand or discontinue operations (real options). The model is used to examine the role of accounting earnings and book value in equity valuation and to explore cross-sectional differences in the behavior of the valuation function. Unlike prior studies (such as Ohlson 1995 and Feltham and Ohlson 1995) where capital investments are either unspecified or exogenously given, capital investment decisions in this model are made contingent on the firm's operating profitability and growth opportunities. Valuation requires first forming beliefs about future capital investments, and then valuing cash flows to be generated from invested assets. Current earnings and book value provide vital information both for forming beliefs about future investments and for forecasting cash flows. With real options, the valuation function emerges as convex, not linear. Specifically, equity value is an increasing and convex function of earnings, for any given book value, but it can be either increasing in, insensitive to, or decreasing in book value, depending on the firm's profitability and growth opportunity. The model leads to predictions regarding the relative importance of earnings versus book value in value determination and how this relative importance varies across firms. It also rationalizes the "anomalous" association between stock prices and negative earnings found in empirical studies (which is due to regression model misspecification). The study further shows how conservative accounting affects the characteristics of earnings and book value, and provides hypotheses regarding how accounting conservatism influences the properties of the valuation function. The predictions of the model are generally consistent with the evidence reported in empirical studies. Implications for empirical research are discussed.

The capital asset pricing model (CAPM) developed by Sharpe (1964) is the starting point for the arbitrage pricing theory (APT). It uses a single risk factor to model the risk premium of an asset class. However, the CAPM has been the subject of important research, which has highlighted numerous empirical contradictions. Based on the APT theory proposed by Ross (1976), Fama and French (1992) and Carhart (1997) introduce other common factors models to capture new risk premia. For instance, they consequently define equity risk factors, such as market, value, size and momentum. In recent years, a new framework based on this literature has emerged to define strategic asset allocation. Similarly, index providers and asset managers now offer the opportunity to invest in these risk factors through factor indexes and mutual funds. These two approaches led to a new paradigm called 'factor investing' (Ang, 2014). Factor investing seems to solve some of the portfolio management issues that emerged in the past, in particular for long-term investors. However, some questions arise, especially with the number of risk factors growing over the last few years (Cochrane, 2011). What is a risk factor? Are all risk factors well-rewarded? What is their level of stability and robustness? How should we allocate between them? The main purpose of this paper is to understand and analyze the factor investing approach in order to answer these questions.

We propose a practical investment framework for dynamic asset allocation across different economic regimes, which we illustrate using a sample of U.S. data from 1948 to 2007. We identify four regimes in the economic cycle and find that these regimes capture pronounced time-variation in the risk and return properties of asset classes. Time-variation is also observed in the risk of a traditional, static strategic asset allocation portfolio. In order to stabilize risk across the economic cycle we propose a dynamic strategic asset allocation approach, which has the potential to enhance expected return as well. The proposed approach is found to be robust to variations in the variable composition of the regime model and can easily be extended with different economic variables and/or additional assets.

We give an overview and outlook of the field of reinforcement learning as it applies to solving financial applications of intertemporal choice. In finance, common problems of this kind include pricing and hedging of contingent claims, investment and portfolio allocation, buying and selling a portfolio of securities subject to transaction costs, market making, asset liability management and optimization of tax consequences, to name a few. Reinforcement learning allows us to solve these dynamic optimization problems in an almost model-free way, relaxing the assumptions often needed for classical approaches.A main contribution of this article is the elucidation of the link between these dynamic optimization problem and reinforcement learning, concretely addressing how to formulate expected intertemporal utility maximization problems using modern machine learning techniques.

The decision-making by individual investors is usually based on their age, education, income, investment portfolio, and other demographic factors. The impact of behavioural aspect of investing is, however, often ignored. The objective of this paper is to explore the impact of behavioural factors and investor’s psychology on their decision-making, and to examine the relationship between investor’s attitude towards risk and behavioural decision-making. The research uses the literature relevant to behavioural decision-making and investor’s psychology. The research is based on the secondary data relating to investments, finance, and economics available on the Internet, previous publications of the author, and some other publications as well. The information is then integrated in order to understand the interrelationships of investor’s perception of risk, behavioural factors, and decision-making in the Indian context. Through this research, the author finds that unlike the classical finance theory suggests, individual investors do not always make rational investment decisions. Their investment decision-making is influenced, to a great extent, by behavioural factors like greed and fear, cognitive dissonance, heuristics, mental accounting, and anchoring. These behavioural factors must be taken into account as risk factors while making investment decisions. Investment advisors and finance professionals must incorporate behavioural issues as risk factors in order to formulate effective investment strategies for individual investors. With an objective to create investor’s confidence in the stock market, behavioural issues are the newest of the things which must be considered while formulating investment strategies. This research will help investment advisors and finance professionals judge investor’s attitude towards risk in a better way, thus leading to better investment decision-making.

This paper investigates the forecasting accuracy of bankruptcy hazard rate models for U.S. companies over the time period 1962 - 1999 using both yearly and monthly observation intervals. The contribution of this paper is multiple-fold. One, using an expanded bankruptcy database we validate the superior forecasting performance of Shumway's (2001) model as opposed to Altman (1968) and Zmijewski (1984). Two, we demonstrate the importance of including industry effects in hazard rate estimation. Industry groupings are shown to significantly affect both the intercept and slope coefficients in the forecasting equations. Three, we extend the hazard rate model to apply to financial firms and monthly observation intervals. Due to data limitations, most of the existing literature employs only yearly observations. We show that bankruptcy prediction is markedly improved using monthly observation intervals. Fourth, consistent with the notion of market efficiency with respect to publicly available information, we demonstrate that accounting variables add little predictive power when market variables are already included in the bankruptcy model.

Cross currency swaps are powerful instruments to transfer assets or liabilities from one currency into another. The market charges for this a liquidity premium, the cross currency basis spread, which should be taken into account by the valuation methodology. We describe and compare two valuation methods for cross currency swaps which are based upon using two different discounting curves. The first method is very popular in practice but inconsistent with single currency swap valuation methods. The second method is consistent for all swap valuations but leads to mark-to-market values for single currency off market swaps, which can be quite different to standard valuation results.

The average Market Risk Premium (MRP) used in 2010 by professors in the USA (6.0%) was higher than the one used by their colleagues in Europe (5.3%). We also report the MRP used for 33 countries: the average MRP used in 2010 ranges from 3.6% (Denmark) to 10.9% (Mexico). 29% of the professors decreased the MRP in 2010, 16% increased it and 55% used the same MRP. The dispersion of the MRP used was high: the average range of MRP used by professors for the same country was 7.4% and the average standard deviation was 2.4%. Most previous surveys have been interested in the Expected MRP, but this survey asks about the Required MRP. The paper also contains the references that professors use to justify their MRP, and comments from 85 professors that illustrate the various interpretations of what is the required MRP.

We consider general stochastic volatility models with no local volatility component and derive the general expression of the volatility smile at order two in volatility-of-volatility. We show how, at this order, the smile only depends on three dimensionless numbers whose precise expressions as functionals of the model's spot/variance and variance/variance covariance functions we provide. Finally we assess the accuracy of our order two expansion using realistic levels of volatility-of-volatility.

This is an advanced course in financial risk management given at the University of Paris-Saclay. The 1700 slides cover the following topics: Lecture 1. Introduction to Financial Risk Management (32 pages)Lecture 2. Market Risk (117 pages)Lecture 3. Credit Risk (196 pages)Lecture 4. Counterparty Credit Risk and Collateral Risk (90 pages)Lecture 5. Operational Risk (75 pages)Lecture 6. Liquidity Risk (34 pages) Lecture 7. Asset Liability Management Risk (113 pages) Lecture 8. Model Risk (197 pages) Lecture 9. Copulas and Extreme Value Theory (84 pages) Lecture 10. Monte Carlo Simulation Methods (189 pages) Lecture 11. Stress Testing and Scenario Analysis (39 pages) Lecture 12. Credit Scoring Models (93 pages)Tutorial Sessions (411 pages)

I show that the recent returns to ESG investing are strongly driven by price impact from flows towards ESG funds. Using data on institutional trades, I estimate the market's ability to accommodate the demand of ESG funds, which is given by the elasticity of substitution between ESG and other stocks. I show that every dollar flowing towards ESG stocks increases their aggregate market value by $0.7. Using a novel measure of total ESG flows, I estimate an annual flow-driven ESG return of 2.07%. In the absence of flows, ESG funds would have not outperformed the market from 2017 to 2022.

This paper examines international differences in firms' cost of equity capital across 40 countries. We analyze whether the effectiveness of a country's legal institutions and securities regulation is systematically related to cross-country differences in the cost of equity capital. We employ several models to estimate firms' implied or ex ante cost of capital. Our results support the conclusion that firms from countries with more extensive disclosure requirements, stronger securities regulation and stricter enforcement mechanisms have a significantly lower cost of capital. We perform extensive sensitivity analyses to assess the potentially confounding influence of countries' long-run growth differences on our results. We also show that, consistent with theory, the cost of capital effects of strong legal institutions become substantially smaller and, in many cases, statistically insignificant as capital markets become globally more integrated.

One of the key assumptions of quantitative trading strategy evaluation is that Type II errors (missed discoveries) are preferable to Type I errors (false discoveries). However, practitioners have known for a long time that the statistical properties of some genuine trading strategies are often indistinguishable from those of random trading strategies. Therefore, any adjustments to statistics to guard against p-hacking increase Type II error unless the power of the test is high. At the same time, the power of the test is limited by insufficient samples and changing market conditions. Furthermore, genuine strategies with statistical properties that are similar to those of random strategies may overfit due to favorable market conditions but fail when market conditions change. These facts severely limit the effectiveness of quantitative claims about trading strategy evaluation. Practitioners have instead resorted to Monte Carlo simulations and stochastic modeling in an effort to increase the chances of identifying robust trading strategies, but these methods also have severe limitations due to changing market conditions, selection bias, and data snooping. In this paper, we present two examples that demonstrate the limitations of quantitative evaluation of trading strategies, and we claim that the most effective way of guarding against overfitting and selection bias is by limiting the applications of backtesting to a class of strategies that employ similar but simple predictors of price. We claim that determining when market conditions change is, in many cases, fundamentally more important than any quantitative claims about trading strategy evaluation.

With recourse to archival, printed primary, and secondary sources, this paper reconstructs global real interest rates on an annual basis going back to the 14th century, covering 78% of advanced economy GDP over time. I show that across successive monetary and fiscal regimes, and a variety of asset classes, real interest rates have not been “stable”, and that since the major monetary upheavals of the late middle ages, a trend decline between 0.6-1.8bps p.a. has prevailed. A consistent increase in real negative-yielding rates in advanced economies over the same horizon is identified, despite important temporary reversals such as the 17th Century Crisis. Against their long-term context, currently depressed sovereign real rates are in fact converging “back to historical trend” – a trend that makes narratives about a “secular stagnation” environment entirely misleading, and suggests that – irrespective of particular monetary and fiscal responses – real rates could soon enter permanently negative territory. I also posit that the return data here reflects a substantial share of “nonhuman wealth” over time: the resulting R-G series derived from this data show a downward trend over the same timeframe: suggestions about the “virtual stability” of capital returns, and the policy implications advanced by Piketty (2014) are in consequence equally unsubstantiated by the historical record.

Cryptocurrency markets exhibit periods of large, recurrent arbitrage opportunities across exchanges. These price deviations are much larger across than within countries, and smaller between cryptocurrencies, highlighting the importance of capital controls for the movement of arbitrage capital. Price deviations across countries co-move and open up in times of large bitcoin appreciation. Countries with higher bitcoin premia over the US bitcoin price see widening arbitrage deviations when bitcoin appreciates. Finally, we decompose signed volume on each exchange into a common and an idiosyncratic component. The common component explains 80% of bitcoin returns. The idiosyncratic components help explain arbitrage spreads between exchanges.

The U.S. equity market changed dramatically in recent years. Increasing automation and the entry of new trading platforms has resulted in intense competition among trading platforms. Despite these changes, traders still face the same challenges as before. They seek to minimize the total cost of trading including commissions, bid/ask spreads, and market impact. New technologies allow traders to implement traditional strategies more effectively. For example, dark pools and indications of interest are just an updated form of tactics that NYSE floor traders used search for counterparties while minimizing the exposure of their clients’ trading interest to prevent front running. Virtually every measurable dimension of U.S. equity market quality has improved. Execution speeds and retail commission have fallen. Bid-ask spreads have fallen and remain low, although they spiked upward along with volatility during the recent financial crisis. Market depth has increased. Studies of institutional transactions costs find U.S. costs among the lowest in the world. Unlike during the Crash of 1987, the U.S. equity market mechanism handled the increase in trading volume and volatility without disruption. However, our markets lack a market-wide risk management system that would deal with computer generated chaos in real time, and our regulators should address this. “Make or take” pricing, the charging of access fees to market orders that “take” liquidity and paying rebates to limit orders that “make” liquidity, causes distortions that should be corrected. Such charges are not reflected in the quotations used for the measurement of best execution. Direct access by non-brokers to trading platforms requires appropriate risk management. Front running orders in correlated securities should be banned.

We introduce a return predictor related to the slope and curvature of the futures term structure: basis-momentum. Basis-momentum strongly outperforms benchmark characteristics in predicting commodity spot and term premiums in the time series and cross section. Exposure to basis-momentum is priced among commodity-sorted portfolios and individual commodities. We argue that basis-momentum captures imbalances in the supply and demand of futures contracts that materialize when the market-clearing ability of speculators and intermediaries is impaired, and that basis-momentum represents compensation for priced risk. Our findings are inconsistent with alternative explanations based on storage, inventory, and hedging pressure.