SSRN Viewer

This paper answers two questions that help those analyzing FinTech understand its origins, growth, and potential to affect financial stability. First, it answers the question of why \"FinTech\" is happening right now. Many of the technologies that support FinTech innovations are not new, but financial institutions and entrepreneurs are only now applying them to financial products and services. Analysis of the supply and demand factors that drive \"traditional\" financial innovation reveals a confluence of factors driving a large quantity of innovation. Second, this paper answers the question of why FinTech is getting so much more attention than traditional innovation normally does. The answer to this question has to do with the 'depth' of innovation, a concept introduced in this paper. The deeper an innovation, the greater the ability of that innovation to transform financial services. The paper shows that many FinTech innovations are deep innovations and hence have a greater potential to change financial services. A greater potential to transform can also lead to a greater chance of affecting financial stability.

This paper performs a census of the 101 performance measures for portfolios that have been proposed so far in the scientific literature. We discuss their main strengths and weaknesses and provide a classification based on their objectives, properties and degree of generalization. The measures are categorized based on the general way they are computed: asset selection vs. market timing, standardized vs. individualized, absolute vs. relative and excess return vs. gain measure. We show that several categories have been exhausted while some others feature very heterogeneous ways to assess performance within the same sets of objectives. Note. The definitive version of this working paper was published by the "Journal of Performance Measurement" in two parts: - “The (more than) 100 Ways to Measure Portfolio Performance - Part 1: Standardized Risk-Adjusted Measures”, Journal of Performance Measurement, Vol. 13, N° 4, Summer 2009, pp. 56-71. - “The (more than) 100 Ways to Measure Portfolio Performance - Part 2: Special Measures and Comparison”, Journal of Performance Measurement, Vol. 14, N° 1, Fall 2009, pp. 56-69.

The bedrock of financial economics is that there should be a tradeoff between risk and reward: an investment with low risk should have a low expected return, while one that could make you rich should also be one which could lose you a lot of money. A lot of research in finance is focused on finding deviations to this risk-reward tradeoff, which are called “market anomalies” in deference to the idea that they are exceptions to this fundamental law of finance. Discovery of an anomaly is usually followed by frenzied debate and research that tries to explain it away as: 1) a statistical fluke, 2) compensation for some hitherto overlooked risk, or 3) some friction in the market which when fixed will make the anomaly go away.The “overnight effect" is one such anomaly, which has been uncovered recently enough that its causes are still being hotly debated among researchers and practitioners. The overnight effect refers to the fact that, over at least the past three decades, investors have earned 100% or more of the return on a wide range of risky assets when the markets are closed, and, as sure as day follows night, have earned zero or negative returns for bearing the risk of owning those assets during the daytime, when markets are open. The effect is seen over a wide range of assets, including the broad stock market, individual stocks (particularly those popular with retail investors, and Meme stocks most of all), many ETFs, and cryptocurrencies.In this article we briefly review the dozen or so papers which have explored this phenomenon to date, which have mostly focused on returns at the level of the broad stock market. We then take a closer look at the behavior of individual US stocks for clues about aggregate stock market behavior. We found that not only did the effect exist at the index level as previously reported, but it also shows up in a suggestively clustered pattern in individual stocks returns, and is particularly strong in “Meme” stocks. We find that a simple long-short portfolio that only takes exposure when the market is closed would have earned a return of 38% per annum (importantly, ignoring transactions costs) with an annualized Sharpe Ratio of about 3. There are good reasons to care about this market anomaly, namely, 1) retail traders are potentiallymissing out on billions of dollars of returns due to mistimed trades, which should concern investors and market regulators alike, 2) there is speculation that the overnight effect might have implications for the long-term valuation of the entire equity market, and, 3) a better understanding of this phenomenon can contribute to our understanding of the limits of market efficiency.

We develop a structural bond valuation model to simultaneously capture liquidity and credit risk. Our model implies that renegotiation in financial distress is influenced by the illiquidity of the market for distressed debt. As default becomes more likely, the components of bond yield spreads attributable to illiquidity increase. When we consider finite maturity debt, we find decreasing and convex term structures of liquidity spreads. Using bond price data spanning 15 years, we find evidence of a positive correlation between the illiquidity and default components of yield spreads as well as support for downward sloping term structures of liquidity spreads.

We present a non-parametric method for calibrating jump-diffusion models to a finite set of observed option prices. We show that the usual formulations of the inverse problem via nonlinear least squares are ill-posed and propose a regularization method based on relative entropy. We reformulate our calibration problem into a problem of finding a risk neutral jump-diffusion model that reproduces the observed option prices and has the smallest possible relative entropy with respect to a chosen prior model. Our approach allows to conciliate the idea of calibration by relative entropy minimization with the notion of risk neutral valuation in a continuous time model. We discuss the numerical implementation of our method using a gradient based optimization algorithm and show via simulation tests on various examples that the entropy penalty resolves the numerical instability of the calibration problem. Finally, we apply our method to datasets of index options and discuss the empirical results obtained.

Our aim is to develop a very offensive (‘aggressive’) tactical asset allocation strategy, by combining some of our previous models like Protected- (PAA), Vigilant- (VAA) and Defensive (DAA) Asset Allocation. We will call this new strategy the ‘Bold Asset Allocation’ (BAA). BAA combines a slow relative momentum with a fast absolute momentum and crash protection, based on the concept of the ‘canary’ universe, where we switch from our offensive to the defensive universe when any of the assets in the canary universe has negative absolute momentum. As a result, BAA spends ca 60% in the defensive universe. By enhancing this defensive universe beyond cash, we find very impressive returns (>=20%) with low monthly max drawdowns (<=15%) over Dec 1970 – Jun 2022.

This paper is concerned with the allegation that fair value accounting rules have contributed significantly to the recent financial crisis. It focuses on one particular channel for that contribution: the impact of fair value on actual or potential failure of banks. The paper compares four criteria for failure: one economic, two legal and one regulatory. It is clear from this comparison that balance sheet valuations of assets are in two cases crucial in these definitions, and so the choice between “fair value” or other valuations can be decisive in whether a bank fails; but in two cases fair value is irrelevant. Bank failures might arise despite capital adequacy and balance sheet solvency due to sudden shocks to liquidity positions. Two of the most prominent bank failures cannot, at first sight, be attributed to fair value accounting: we show that Northern Rock was balance sheet solvent, even on a fair value basis, as was Lehman Brothers. The anecdotal evidence is augmented by empirical tests that suggest that mark-to-market accounting does not increase the perceived bankruptcy risk of banks.

We study the impact of algorithmic trading in the foreign exchange market using a long time series of high-frequency data that specifically identifies computer-generated trading activity. Using both a reduced-form and a structural estimation, we find clear evidence that algorithmic trading causes an improvement in two measures of price efficiency in this market: the frequency of triangular arbitrage opportunities and the autocorrelation of high-frequency returns. Relating our results to the recent theoretical literature on the subject, we show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity, while the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence that algorithmic traders do not trade with each other as much as a random matching model would predict, which we view as consistent with their trading strategies being highly correlated. However, the analysis shows that this high degree of correlation does not appear to cause a degradation in market quality.

Existing research indicates that it is possible to forecast potential long-term returns in the S&P 500 for periods of more than 10 years using the cyclically adjusted price-to-earnings ratio (CAPE). This paper concludes that this relationship has also existed internationally in 17 MSCI Country indexes since 1979. In addition, the paper also examines the forecasting ability of price-to-earnings, price-to-cash-flow and price-to-book ratio, as well as that of dividend yield and of CAPE adjusted for changes in payout ratios. The results indicate that only price-to-book ratio and CAPE enable reliable forecasts on subsequent returns and market risks. In countries with structural breaks, price-to-book ratio even exhibits some advantages compared to CAPE. Based on these findings, the long-term equity market potential for various markets is forecasted using CAPE and price-to-book ratio. The current valuation makes it likely that investors with a global portfolio can achieve real returns of 6% over the next 10 to 15 years. Even greater increases can be expected in European equity markets (8%) and in emerging markets (9%). Due to the high valuation of the US stock market, US investors can only expect below-average returns of 4% with a higher drawdown potential.

In event study analyses of abnormal returns on a single day, Corrado's (1989) nonparametric rank test and its modification in Corrado and Zivney (1992) have good empirical power properties, but problems arise in their application to cumulative abnormal returns (CARs). This paper proposes a generalized rank (GRANK) testing procedure that can be used for testing both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived and empirical properties of the test statistics are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to parametric tests by Patell (1976) and Boehmer, Musumeci, and Poulsen (1991).

Textual analysis, implemented at scale, has become an important addition to the methodological toolbox of finance. In this paper, given the proliferation of papers now using this method, we first provide an updated review of the literature while focusing on a few broad topics—social media, political bias, and detecting fraud. While we do not attempt to survey the various methods, we focus on the construction and use of lexicons in finance. We then center the discussion on readability as an attribute frequently incorporated in contemporaneous research, arguing that its use begs the question of what we are measuring. Finally, we discuss how the literature might build on the intent of measuring readability to measure something more appropriate and more broadly relevant—complexity.

This paper examines the historical performance of 12 portfolios that include S&P 100/500 index options. Each option portfolio is formed using options with different maturities and moneyness, while incorporating bid-ask spreads, transaction costs, and margin requirements. Raw and risk-adjusted returns of option portfolios are compared to a benchmark portfolio that is only long the underlying asset. This allows the marginal impact of including options in the portfolio to be examined. The analysis reveals that including options in the portfolio most often results in underperformance relative to the benchmark portfolio. However, a portfolio that incorporates written options can outperform the benchmark on a raw and risk-adjusted basis. This result is dependent on restricting option investment relative to the maximum allowable margin. While positive and significant risk-adjusted performance is observed for some option portfolios, greater risk tolerance relative to the long index benchmark portfolio is required.

Execution traders know that market impact greatly depends on whether their orders lean with or against the market. We introduce the OEH model, which incorporates this fact when determining the optimal trading horizon for an order, an input required by many sophisticated execution strategies. From a theoretical perspective, OEH explains why market participants may rationally “dump” their orders in an increasingly illiquid market. OEH is shown to perform better than participation rate schemes and VWAP strategies. We argue that trade side and order imbalance are key variables needed for modeling market impact functions, and their dismissal may be the reason behind the apparent disagreement in the literature regarding the functional form of the market impact function. Our backtests suggest that OEH contributes substantial 'execution alpha' for a wide variety of futures contracts. An implementation of OEH is provided in Python language.

In this paper, I describe some of the main parametric and non-parametric tests used in event studies to assess the significance of abnormal returns or changes in variance of returns. The parametric tests here described are Brown and Warner's test (1980, 1985), with and without crude independence adjustment, Patell's (1976) standardised residual test and Boehmer, Musumeci and Poulsen's (1991) standardised cross-sectional test. The non-parametric tests are the generalised sign test (Sanger and McConnell, 1986 and Cowen and Sergeant, 1996), the Wilcoxson signed rank test and Corrado's (1989) rank test. In addition I outline briefly a bootstrapping procedure for the purpose of testing non-normal distributed abnormal returns. I also describe four tests to evaluate the significance of changes in variance. I present two parametric tests (an F test for the equality of two population variances and the Beavers's/May's U test) and two non-parametric tests (the squared rank test and another rank test proposed by Rohrbach and Chandra, 1989).

Drawing on insights of current and past editors of top economics and finance journals, we provide guidelines for reviewers in preparing referee reports and cover letters for journals. Peer review is fundamental to the progress of science and we believe that fundamental changes in reviewing practices are needed to improve the integrity, quality, and efficiency of the publication process. Such changes will also allow scholars to reallocate time from navigating the publication process to developing innovative research. Also see our companion paper: Preparing a Referee Report: Guidelines and Perspectives

The use of the Return On Capital Employed (ROCE) as a performance indicator is questioned in this paper. The paper is using the premise that performance indication can only be meaningful to the user if it bears a true reflection of the relationship that it intends to measure. An empirical evaluation of financial statements of 16 firms listed on the Nigerian Stock Exchange reveal that ROCE as currently defined presents distorted and misleading financial ratio which bears no relationship with the actual capital usage of a firm. It also revealed that a true measure of efficiency in the use of capital resources cannot be done using capital employed as defined in a company's balance sheet. This is because the balance sheet capital is a static measure of capital employed at a date and not for the entire period. Hence, the result to be obtained from such measurement would invariably be influenced by the static nature of the value of capital employed as at that date. A more refined approach considered the firm's actual capital turnover (usage) ratio and presents an Enhanced Return On Capital Employed (EROCE) ratio measurement that correlates better with a firm's capital employed using the Spearman's correlation coefficient formula. The study recommends the replacement of the ROCE with EROCE for maximum effectiveness.

We give a general treatment of the Vanna-Volga mark-to-market volatility smile correction in application to pricing of contracts with European exercise on a single underlying. The method remains applicable in cases of delayed or misaligned expiries and absolute dividends. It is also applied to cases of time-dependent instantaneous volatility, multiple underlying assets and random interest rates. We also offer computation of the underlying volatility from market data and most valuable correction using more than three traded options.

Fama and French (2015) propose to augment their classic (1993) 3-factor model with profitability and investment factors, resulting in a 5-factor model, which is likely to become the new benchmark for asset pricing studies. Although the 5-factor model exhibits significantly improved explanatory power, we identify five concerns with regard to the new model. First, it maintains the CAPM relation between market beta and return, despite mounting evidence that the empirical relation is flat, or even negative. Second, it continues to ignore the, by now, widely accepted momentum effect. Third, there are a number of robustness concerns with regard to the two new factors. Fourth, whereas risk-based explanations were key for justifying the factors in the 3-factor model, the economic rationale for the two new factors is much less clear. Fifth and finally, it does not seem likely that the 5-factor model is going to settle the main asset pricing debates or lead to consensus.

This paper proposes a new approach to investing for retirement that takes advantage of recent market innovations and advances in finance theory to improve the risk/reward opportunities available to individual investors before and after retirement. The approach introduces three new elements: - It uses inflation-protected bonds to hedge a minimum standard of living after retirement. - It takes account of a person's willingness to postpone retirement. - It uses option "ladders" to lever growth in retirement income.

Our paper reexamines whether 29 variables from 26 papers published after Goyal and Welch (2008), as well as the original 17 variables, were useful in predicting the equity premium in-sample and out-of-sample as of the end of 2021. Our samples include the original periods in which these variables were identified, but end later. About half of the new variables have no empirical significance even in-sample. Of those that do, about half have poor out-of-sample performance. A small number of variables still perform reasonably well both in-sample and out-of-sample.

Several studies report that abnormal returns associated with short-term reversal investment strategies diminish once transaction costs are taken into account. We show that the impact of transaction costs on the strategies’ profitability can largely be attributed to excessively trading in small cap stocks. Limiting the stock universe to large cap stocks significantly reduces trading costs. Applying a more sophisticated portfolio construction algorithm to lower turnover reduces trading costs even further. Our finding that reversal strategies generate 30 to 50 basis points per week net of transaction costs poses a serious challenge to standard rational asset pricing models. Our findings also have important implications for the understanding and practical implementation of reversal strategies.

A substitution effect takes place when two or more explanatory variables share a substantial amount of information (predictive power).Under the presence of substitution effects, feature importance methods may not be able to determine robustly which variables are significant.This presentation discusses the Clustered Feature Importance (CFI) method, which is robust to linear as well as non-linear substitution effects.

We develop a novel ranking methodology to rank the market forecaster. In particular, we distinguish forecasts by their specificity, rather than considering all predictions and forecasts equally important, and we also analyze the impact of the number of forecasts made by a particular forecaster. We have applied our methodology on a dataset including 6,627 forecasts made by 68 forecasters.

We examine the relation between indexing and active management in the mutual fund industry worldwide. Explicit indexing and closet indexing by active funds are associated with countries’ regulatory and financial market environments. We find that actively managed funds are more active and charge lower fees when they face more competitive pressure from low-cost explicitly indexed funds. A quasi-natural experiment using the exogenous variation in indexed funds generated by the passage of pension laws supports a causal interpretation of the results. Moreover, the average alpha generated by active management is higher in countries with more explicit indexing and lower in countries with more closet indexing. Overall, our evidence suggests that explicit indexing improves competition in the mutual fund industry.

We examine the quantification of operational risk for banks. We adopt a financial economics approach and interpret operational risk management as a means of optimizing the profitability of an institution along its value chain. We start by defining operational risk and then propose a framework to model risk mitigation through the bank's value chain over time. Using analytical and numerical methods, we obtain answers concerning capital allocation, network stability, risk figures, and diversification issues. Interpreting the results shows that the usual intuition gained from market and credit risk does not apply to the quantification of operational risk.