Return Predictability and Market Sentiment: Evidence from Deep Learning

Recent studies in asset pricing find that Artificial Neural Networks (also known as Deep Learning models) provide the most accurate firm-level return predictions using a vast set of predictive signals. These models offer high predictive accuracy over long out-of-sample periods, translating into highly profitable trading strategies. In this thesis, I argue that sentiment-driven mispricing is a vital source of the high predictability and the resulting profitability implied by deep learning models. Using a novel Artificial Neural Network (ANN) regression model, I obtain firm-level predictions conditional on 54 firm-level characteristics and on an encoded representation of the macro-economic state. These predictions provide important insights into the sources of overall cross-sectional return predictability. First, the future negative returns are predictable out-of-sample which implies negative expected returns. Such predictability is hard to reconcile with a risk-based explanation. Secondly, the predictability in negative returns is higher following periods of high sentiment and vice versa. This evidence is consistent with the existence of a market-level investor sentiment that drives misvaluations. Third, a long-short strategy based on ANN prediction deciles is more profitable following periods of high sentiment. This disparity in profitability points to arbitrage asymmetry implied by short-sale constraints. Fourth, the predictability in losses and high profitability of the ANN top decile vanishes in estimation horizons longer than a month. This suggests that mispricing is short-lived and that predictability is realized due to corrections to such misvaluations. These corrections are preceded by high put-to-call(PCR) trading volumes and high implied volatility(VIX). Finally, the short-term and long-term predictions load on different conditioning variables indicating varying sources of predictability across return horizons. Overall, these findings are consistent with the existence of sentiment-driven short-lived mispricing that corrects in longer horizons

A Protocol for Factor Identification

We propose a protocol for identifying genuine risk factors. The underlying premise is that a risk factor must be related to the covariance matrix of returns, must be priced in the cross-section of returns, and should yield a reward-to-risk ratio that is reasonable enough to be consistent with risk pricing. A market factor, a profitability factor, and traded versions of macroeconomic factors pass our protocol, but many characteristic-based factors do not. Several of the underlying characteristics, however, do command premiums in the cross-section

LONG-RUN INDUSTRY EFFECT ON STOCK RETURN: AN EMPIRICAL EVALUATION OF SELECTED NIGERIAN BANKS

The study develops a fresh econometric equation to estimate the nature of the relationship between banking industry activities and stock market returns in Nigeria. The equation utilizes annual data sourced from the various volumes of Nigerian Stock Exchange (NSE) Fat books, NSE Daily Official List and annual reports of the selected banks for a period of 25 years ranging from 1984 to 2009. Our findings reveal that the activities of the banking industry and stock market return maintain a long-run relationship. Furthermore, we discover that an increase in earning produces a positive multiplier effect on stock market return; while retention of earning for acquisition of assets and high level of debt/leverage ratio is found to be detrimental to stock prices here in Nigeria

Style anomalies on the Toronto Stock Exchange : a univariate, multivariate, style timing and portfolio sorting analysis

Includes bibliographical references.A growing body of empirical evidence has found inconsistencies in the Capital Asset-pricing Model (CAPM) of Sharpe (1964), Lintner (1965), and Black (1972) and Ross's (1976) Arbitrage Pricing Theory (APT). Numerous attempts to explore the validity of these theories of modern finance have led to the identification of various firm specific attributes that explain the cross-sectional variation of returns. These attributes have appropriately been termed 'style anomalies '.This thesis investigates the existence and exploitability of style anomalies for the shares comprising the Toronto Stock Exchange (TSX) for the period 31 January 1989 to 31 July 2005. The investigation is divided into four areas of research. First, a methodology similar to Fama and Macbeth (1973) is used to explore the cross-sectional relationships between some 904 firm-specific attributes and the unadjusted and risk adjusted monthly returns of equities constituting the S&P TSX Composite Index. A myriad of uncorrelated style anomalies are found to persist before and after controlling for systematic risk, and are categorized as either size, growth, momentum, value, liquidity and bankruptcy (risk) effects. The most significant attributes from each respective style group include: Price, eighteen month change in net tangible asset value, price change over twelve months, twelve month change in price to net tangible asset value, three month change in the absolute volume ratio and interest cover before tax. Multivariate testing confirms the ability of anomalies to explain excess returns. In and out sample cross sectional tests show inconsistent anomaly persistence, raising the question of whether they are perhaps perennial in nature. Second, the predictability of style payoffs is examined through the analysis of autocorrelation and six style timing models. Strong positive autocorrelation at lower orders for the majority of style payoffs suggests that the ability to time payoffs is possible. The six month moving average timing model shows the best forecasting skill, followed by twelve month and eighteen month moving average models. Third, the presence of firm specific attributes among three classified sectors namely: Basic materials, Cyclicals and Non-Cyclicals are compared. Risk, value and liquidity based anomalies dominate the Basic Materials shares. Liquidity effects stand out within the Cyclicals group, and the Non-Cyclicals sectors exhibit value and size effects. The ability to exploit all style-based anomalies after accounting for transaction costs is evaluated using a portfolio sorting methodology. The tests illustrate that increased exposure to the anomalies has delivered substantially higher returns with lower volatility than a buy and hold approach using an equally weighted all share benchmark. These abnormal returns are confirmed after adjusting for systematic risk. Further testing shows that the attributes, rather than loading on those attributes, are better at explaining share returns. Finally, the seasonal nature of Canadian equity returns is investigated. A six month strategy of "Selling in June and going away till December" provides the most optimal returns. The calendar month tests find January, February and December to be the strongest months of the year. Attribute payoffs seem to show vague seasonal tendencies

Benefits of a Tree-Based model for stock selection in a South African context

Includes bibliographical references.Quantitative investment practitioners typically model the performance of a stock relative to its benchmark and the stock's fundamental factors in a classical linear framework. However, these models have empirically been found to be unsuitable for capturing higher-order relationships between a stock's return relative to a benchmark and its fundamental factors. This dissertation studies the use of Classification and Regression Tree (CART) models for stock selection within the South African context, with the focus being on the period from when the Global Financial Crisis began in early 2007 until December 2012. By utilising four types of portfolios, a CART model is directly compared against two traditional linear models. It is seen that during the period focused upon, the portfolios based on the CART model deliver the best excess return and risk-adjusted return, albeit in most cases modestly above the returns delivered by the portfolios based upon the linear models. This is observed in the hedge-fund style and long-only portfolios constructed. Moreover, it is observed that the CART-based portfolios' returns are not correlated with those from the linear-model-based portfolios. This observation suggests that CART models offer an attractive option to diversify model risk within the South African context

Robust asset allocation under model ambiguity

A decision maker, when facing a decision problem, often considers several models to represent the outcomes of the decision variable considered. More often than not, the decision maker does not trust fully any of those models and hence displays ambiguity or model uncertainty aversion. In this PhD thesis, focus is given to the specific case of asset allocation problem under ambiguity faced by financial investors. The aim is not to find an optimal solution for the investor, but rather come up with a general methodology that can be applied in particular to the asset allocation problem and allows the investor to find a tractable, easy to compute solution for this problem, taking into account ambiguity. This PhD thesis is structured as follows: First, some classical and widely used models to represent asset returns are presented. It is shown that the performance of the asset portfolios built using those single models is very volatile. No model performs better than the others consistently over the period considered, which gives empirical evidence that: no model can be fully trusted over the long run and that several models are needed to achieve the best asset allocation possible. Therefore, the classical portfolio theory must be adapted to take into account ambiguity or model uncertainty. Many authors have in an early stage attempted to include ambiguity aversion in the asset allocation problem. A review of the literature is studied to outline the main models proposed. However, those models often lack flexibility and tractability. The search for an optimal solution to the asset allocation problem when considering ambiguity aversion is often difficult to apply in practice on large dimension problems, as the ones faced by modern financial investors. This constitutes the motivation to put forward a novel methodology easily applicable, robust, flexible and tractable. The Ambiguity Robust Adjustment (ARA) methodology is theoretically presented and then tested on a large empirical data set. Several forms of the ARA are considered and tested. Empirical evidence demonstrates that the ARA methodology improves portfolio performances greatly. Through the specific illustration of the asset allocation problem in finance, this PhD thesis proposes a new general methodology that will hopefully help decision makers to solve numerous different problems under ambiguity

Multiobjective genetic programming for financial portfolio management in dynamic environments

Multiobjective (MO) optimisation is a useful technique for evolving portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk. The resulting Pareto front would approximate the risk/reward Efficient Frontier [Mar52], and simplifies the choice of investment model for a given client’s attitude to risk. However, the financial market is continuously changing and it is essential to ensure that MO solutions are capturing true relationships between financial factors and not merely over fitting the training data. Research on evolutionary algorithms in dynamic environments has been directed towards adapting the algorithm to improve its suitability for retraining whenever a change is detected. Little research focused on how to assess and quantify the success of multiobjective solutions in unseen environments. The multiobjective nature of the problem adds a unique feature to be satisfied to judge robustness of solutions. That is, in addition to examining whether solutions remain optimal in the new environment, we need to ensure that the solutions’ relative positions previously identified on the Pareto front are not altered. This thesis investigates the performance of Multiobjective Genetic Programming (MOGP) in the dynamic real world problem of portfolio optimisation. The thesis provides new definitions and statistical metrics based on phenotypic cluster analysis to quantify robustness of both the solutions and the Pareto front. Focusing on the critical period between an environment change and when retraining occurs, four techniques to improve the robustness of solutions are examined. Namely, the use of a validation data set; diversity preservation; a novel variation on mating restriction; and a combination of both diversity enhancement and mating restriction. In addition, preliminary investigation of using the robustness metrics to quantify the severity of change for optimum tracking in a dynamic portfolio optimisation problem is carried out. Results show that the techniques used offer statistically significant improvement on the solutions’ robustness, although not on all the robustness criteria simultaneously. Combining the mating restriction with diversity enhancement provided the best robustness results while also greatly enhancing the quality of solutions

Are the dynamic linkages between the macroeconomy and asset prices time-varying?

We estimate a number of multivariate regime switching VAR models on a long monthly data set for eight variables that include excess stock and bond returns, the real T-bill yield, predictors used in the finance literature (default spread and the dividend yield), and three macroeconomic variables (inflation, real industrial production growth, and a measure of real money growth). Heteroskedasticity may be accounted for by making the covariance matrix a function of the regime. We find evidence of four regimes and of time-varying covariances. We provide evidence that the best in-sample fit is provided by a four state model in which the VAR(1) component fails to be regime-dependent. We interpret this as evidence that the dynamic linkages between financial markets and the macroeconomy have been stable over time. We show that the four-state model can be helpful in forecasting applications and to provide one-step ahead predicted Sharpe ratios.Macroeconomics ; Asset pricing