This document results from a research project funded by the Cyber-Defence Campus, armasuisse Science and Technology. We appreciate helpful comments from seminar participants at the Cyber Alp Retreat 2023. We also thank Pierre Collin-Dufresne and Michel Dubois for their invaluable comments
We extract firms' cyber risk with a machine learning algorithm measuring the proximity between their disclosures and a dedicated cyber corpus. Our approach outperforms dictionary methods, uses full disclosure and not devoted-only sections, and generates a cyber risk measure uncorrelated with other firms' characteristics. We find that a portfolio of US-listed stocks in the high cyber risk quantile generates an excess return of 18.72% p.a. Moreover, a long-short cyber risk portfolio has a significant and positive risk premium of 6.93% p.a., robust to all factors' benchmarks. Finally, using a Bayesian asset pricing method, we show that our cyber risk factor is the essential feature that allows any multi-factor model to price the cross-section of stock returns.
Optimal capital allocation between different assets is an important financial problem, which is generally framed as the portfolio optimization problem. General models include the single-period and multi-period cases. The traditional Mean-Variance model introduced by Harry Markowitz has been the basis of many models used to solve the portfolio optimization problem. The overall goal is to achieve the highest return and lowest risk in portfolio optimization problems. In this paper, we will present an optimal portfolio based the Markowitz Mean-Variance-Skewness with weight constraints model for sh…
We uncover networks from news articles to study cross-sectional stock returns. By analyzing a huge dataset of more than 1 million news articles collected from the internet, we construct time-varying directed networks of the S&P500 stocks. The well-defined directed news networks are formed based on a modest assumption about firm-specific news structure, and we propose an algorithm to tackle type-I errors in identifying the stock tickers. We find strong evidence for the comovement effect between the news-linked stocks returns and reversal effect from the lead stock return on the 1-day ahead foll…
We analyze correlations among stock returns via a series of widely adopted parameters which we refer to as explanatory variables. We subsequently exploit the results to propose a long only quantitative adaptive technique to construct a profitable portfolio of assets which exhibits minor drawdowns and higher recoveries than both an equally weighted and an efficient frontier portfolio.
It is customary that when security prices fully reflect all available information, the markets for those securities are said to be efficient. And if markets are inefficient, investors can use available information ignored by the market to earn abnormally high returns on their investments. In this context this paper tries to find evidence supporting the reality of weak-form efficiency of the Dhaka Stock Exchange (DSE) by examining the issues of market risk-return relationship and market depth or liquidity for DSE. The study uses a data set of daily market index and returns for the period of 199…
There is an important literature focused on profit warnings and its impact on stock returns. We provide evidence from Moroccan stock market which aims to become an African financial hub. Despite this practical improvement, academic researches that focused on this market are scarce and our study is a first investigation in this context. Using the event study methodology and a sample of companies listed in Casablanca Stock Exchange for the period of 2009 to 2016, we examined whether the effect of qualitative warning is more negative compared to quantitative warnings in a short event window. Our …
For researching the association between coal enterprise management and return in financial market, this paper applies the method of time difference relevance and PageRank method to seek the leader-index of a stock set containing 21 coal enterprises in A-share market and score those stocks. Based on the return in 2011, the asynchronism of the return series is revealed and presents a hierarchical structure of our stock set. Finally, we compare the result with the firm-level variables and discuss the relation between them. The results show that those large coal enterprises with a good management …
HEC Lausanne, University of Lausanne（洛桑大学高等商学院）；Swiss Finance Institute（瑞士金融研究所）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；armasuisse S+T（瑞士武器采购局科技分部）
We describe a new framework for causal inference and its application to return time series. In this system, causal relationships are represented as logical formulas, allowing us to test arbitrarily complex hypotheses in a computationally efficient way. We simulate return time series using a common factor model, and show that on this data the method described significantly outperforms Granger causality (a primary approach to this type of problem). Finally we apply the method to real return data, showing that the method can discover novel relationships between stocks. The approach described is a…
University of Technology Sydney（悉尼科技大学）；University of Sydney（悉尼大学）
We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time-varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly U.S. stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and …
This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The "risk view", whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation for risk exposure. This view is consistent with the Efficient Market Hypothesis. The other view is the "behavioral view", which states that some investors persistently underestimate the true value of high quality firms. We find no evidence in favor of the "risk view": The returns…
Designing an optimum portfolio that allocates weights to its constituent stocks in a way that achieves the best trade-off between the return and the risk is a challenging research problem. The classical mean-variance theory of portfolio proposed by Markowitz is found to perform sub-optimally on the real-world stock market data since the error in estimation for the expected returns adversely affects the performance of the portfolio. This paper presents three approaches to portfolio design, viz, the minimum risk portfolio, the optimum risk portfolio, and the Eigen portfolio, for seven important …
Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical economic sectors of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Sector-wise portfolios are built based on their ten most significant stocks. An LSTM model is also designed for predicting future stock …
It is widely recognized that when classical optimal strategies are applied with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time. The predominant explanation for this is the difficulty of estimating expected returns accurately. In this paper, we modify the $n$ stock Black--Scholes model by introducing a new parametrization of the drift rates. We solve Markowitz' continuous time portfolio problem in this framework. The optimal portfolio weights correspond to keeping $1/n$ of the wealth invested in stocks in each of the $n$ Brownian m…
Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector. An L…
