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on Financial Markets |
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Issue of 2022‒10‒31
eleven papers chosen by |
| By: | Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We analyze the predictive value of state-level business applications, as a proxy of local investor sentiment, for the state-level realized US stock-market volatility. We use highfrequency data for the period from September, 2011 to October, 2021 to compute realized volatility. We show, using an extended version of the popular heterogenous autoregressive realized volatility model, that business applications have predictive value at intermediate and long forecast horizons, after controlling for realized moments (realized skewness, realized kurtosis, realized tail risks), for realized state-level stock-market volatility, and for upside (``good") and downside (``bad") realized volatility. |
| Keywords: | State-level stock markets, State-level investor sentiment, Business applications, Realized volatility, Forecasting |
| JEL: | C22 C53 G10 G17 G41 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202247&r= |
| By: | Sudeep R. Bapat; Saumya Kothari; Rushil Bansal |
| Abstract: | In this paper, we look at the impact of Environment, Social and Governance related news articles and social media data on the stock market performance. We pick four stocks of companies which are widely known in their domain to understand the complete effect of ESG as the newly opted investment style remains restricted to only the stocks with widespread information. We summarise live data of both twitter tweets and newspaper articles and create a sentiment index using a dictionary technique based on online information for the month of July, 2022. We look at the stock price data for all the four companies and calculate the percentage change in each of them. We also compare the overall sentiment of the company to its percentage change over a specific historical period. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.00731&r= |
| By: | R\'emi J\'ez\'equel (USC); Dmitrii M. Ostrovskii (USC); Pierre Gaillard |
| Abstract: | In the problem of online portfolio selection as formulated by Cover (1991), the trader repeatedly distributes her capital over $ d $ assets in each of $ T > 1 $ rounds, with the goal of maximizing the total return. Cover proposed an algorithm, termed Universal Portfolios, that performs nearly as well as the best (in hindsight) static assignment of a portfolio, with an $ O(d\log(T)) $ regret in terms of the logarithmic return. Without imposing any restrictions on the market this guarantee is known to be worst-case optimal, and no other algorithm attaining it has been discovered so far. Unfortunately, Cover's algorithm crucially relies on computing certain $ d $-dimensional integral which must be approximated in any implementation; this results in a prohibitive $ \tilde O(d^4(T+d)^{14}) $ per-round runtime for the fastest known implementation due to Kalai and Vempala (2002). We propose an algorithm for online portfolio selection that admits essentially the same regret guarantee as Universal Portfolios -- up to a constant factor and replacement of $ \log(T) $ with $ \log(T+d) $ -- yet has a drastically reduced runtime of $ \tilde O(d^2(T+d)) $ per round. The selected portfolio minimizes the current logarithmic loss regularized by the log-determinant of its Hessian -- equivalently, the hybrid logarithmic-volumetric barrier of the polytope specified by the asset return vectors. As such, our work reveals surprising connections of online portfolio selection with two classical topics in optimization theory: cutting-plane and interior-point algorithms. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.13932&r= |
| By: | Jaydip Sen; Abhishek Dutta |
| Abstract: | Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This paper presents a systematic approach to portfolio optimization using two approaches, the hierarchical risk parity algorithm and the Eigen portfolio on seven sectors of the Indian stock market. The portfolios are built following the two approaches to historical stock prices from Jan 1, 2016, to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021, to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of its Eigen counterpart on both training and test data for the majority of the sectors studied. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.00984&r= |
| By: | \'Alvaro Rubio-Garc\'ia; Juan Jos\'e Garc\'ia-Ripoll; Diego Porras |
| Abstract: | Portfolio optimization is an important process in finance that consists in finding the optimal asset allocation that maximizes expected returns while minimizing risk. When assets are allocated in discrete units, this is a combinatorial optimization problem that can be addressed by quantum and quantum-inspired algorithms. In this work we present an integer simulated annealing method to find optimal portfolios in the presence of discretized convex and non-convex cost functions. Our algorithm can deal with large size portfolios with hundreds of assets. We introduce a performance metric, the time to target, based on a lower bound to the cost function obtained with the continuous relaxation of the combinatorial optimization problem. This metric allows us to quantify the time required to achieve a solution with a given quality. We carry out numerical experiments and we benchmark the algorithm in two situations: (i) Monte Carlo instances are started at random, and (ii) the algorithm is warm-started with an initial instance close to the continuous relaxation of the problem. We find that in the case of warm-starting with convex cost functions, the time to target does not grow with the size of the optimization problem, so discretized versions of convex portfolio optimization problems are not hard to solve using classical resources. We have applied our method to the problem of re-balancing in the presence of non-convex transaction costs, and we have found that our algorithm can efficiently minimize those terms. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.00807&r= |
| By: | Yufeng Huang (Simon Business School, University of Rochester, Rochester, NY 14627); Bowen Luo (D’Amore-McKim School of Business, Northeastern University, Boston, MA 02115); Chenyu Yang (3115E Tydings Hall, 7343 Preinkert Dr., University of Maryland, College Park, MD 20742) |
| Abstract: | Non-fungible Tokens (NFT) have emerged as a popular monetization mechanism for digital artists. We study the NFT market on foundation.app, an NFT platform. We document significant heterogeneity of seller behaviors. 6.4% of sellers transfer out their proceeds to a crypto exchange, but they account for 26.4% of all artwork sales. We also find demand is not correlated with ether prices, but ether prices affect the listing prices set by sellers that do not transfer out proceeds. We conjecture that sellers that rely on NFT sales for income are better informed of the demand. We study the implications of the ether price volatility for market efficiency. |
| Keywords: | NFT, crypto price volatility, market efficiency |
| JEL: | D4 P4 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:2207&r= |
| By: | Andrew Y. Chen; Tom Zimmermann |
| Abstract: | Researchers are more likely to share notable findings. As a result, published findings tend to overstate the magnitude of real-world phenomena. This bias is a natural concern for asset pricing research, which has found hundreds of return predictors and little consensus on their origins. Empirical evidence on publication bias comes from large scale meta-studies. Meta-studies of cross-sectional return predictability have settled on four stylized facts that demonstrate publication bias is not a dominant factor: (1) almost all findings can be replicated, (2) predictability persists out-of-sample, (3) empirical $t$-statistics are much larger than 2.0, and (4) predictors are weakly correlated. Each of these facts has been demonstrated in at least three meta-studies. Empirical Bayes statistics turn these facts into publication bias corrections. Estimates from three meta-studies find that the average correction (shrinkage) accounts for only 10 to 15 percent of in-sample mean returns and that the risk of inference going in the wrong direction (the false discovery rate) is less than 6%. Meta-studies also find that t-statistic hurdles exceed 3.0 in multiple testing algorithms and that returns are 30 to 50 percent weaker in alternative portfolio tests. These facts are easily misinterpreted as evidence of publication bias effects. We clarify these misinterpretations and others, including the conflating of "mostly false findings" with "many insignificant findings," "data snooping" with "liquidity effects," and "failed replications" with "insignificant ad-hoc trading strategies." Meta-studies outside of the cross-sectional literature are rare. The four facts from cross-sectional meta-studies provide a framework for future research. We illustrate with a preliminary re-examination of equity premium predictability. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.13623&r= |
| By: | Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We analyze the predictive value of climate risks for state-level realized stock-market volatility, computed, along with other realized moments, based on high-frequency intra-day U.S. data (September, 2011 to October, 2021). A model-based bagging algorithm recovers that climate risks have predictive value for realized volatility at intermediate and long (one and two months) forecast horizons. This finding also holds for upside (``good" ) and downside (``bad" ) realized volatility. The benefits of using climate risks for forecasting state-level realized stock-market volatility depend on the shape and (as-)symmetry of a forecaster's loss function. |
| Keywords: | State-level data, Realized stock-market volatility, Climate-related predictors, Forecasting |
| JEL: | C22 C53 G10 G17 Q54 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202246&r= |
| By: | Francesca Biagini; Lukas Gonon; Andrea Mazzon; Thilo Meyer-Brandis |
| Abstract: | In this paper we employ deep learning techniques to detect financial asset bubbles by using observed call option prices. The proposed algorithm is widely applicable and model-independent. We test the accuracy of our methodology in numerical experiments within a wide range of models and apply it to market data of tech stocks in order to assess if asset price bubbles are present. In addition, we provide a theoretical foundation of our approach in the framework of local volatility models. To this purpose, we give a new necessary and sufficient condition for a process with time-dependent local volatility function to be a strict local martingale. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.01726&r= |
| By: | Chen Zhang (SenseTime Research) |
| Abstract: | Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing. I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set of predictive signals (firm characteristics, systematic risks and macroeconomics). I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep learning methods, and figure out that RNNs with memory mechanism and attention have the best performance in terms of predictivity. Furthermore, I demonstrate large economic gains to investors using deep learning forecasts. The results of my comparative experiments highlight the importance of domain knowledge and financial theory when designing deep learning models. I also show return prediction tasks bring new challenges to deep learning. The time varying distribution causes distribution shift problem, which is essential for financial time series prediction. I demonstrate that deep learning methods can improve asset risk premium measurement. Due to the booming deep learning studies, they can constantly promote the study of underlying financial mechanisms behind asset pricing. I also propose a promising research method that learning from data and figuring out the underlying economic mechanisms through explainable artificial intelligence (AI) methods. My findings not only justify the value of deep learning in blooming fintech development, but also highlight their prospects and advantages over traditional machine learning methods. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.12014&r= |
| By: | Maran, Raluca |
| Abstract: | This paper uses an event study analysis to assess how stock prices in the Philippines have reacted to domestic monetary-policy changes using data at a daily frequency from 2017 to 2022. A major contribution of this paper is the construction of a monetary-policy surprise measure for the Philippines, as the difference between the actual change in the monetary policy rate and the change anticipated by professional forecasters. My results are consistent with the literature, suggesting that unanticipated monetary policy changes exert a significant influence on stock prices in the Philippines. Overall, I find that an unexpected increase of 25 basis points in the monetary policy rate increases stock prices by about 1.09% on average. These results are robust to the inclusion of additional control variables in the baseline regression model, such as the implementation of restrictions to economic activity to curb the spread of the COVID-19 outbreak or revisions to macroeconomic forecasts released concomitantly with the monetary-policy rate announcement. |
| Keywords: | Event study; government policy responses; monetary policy surprise; Philippines; stock market returns. |
| JEL: | E52 G14 |
| Date: | 2022–10–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114855&r= |