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on Financial Markets |
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Issue of 2022‒03‒07
thirteen papers chosen by |
| By: | Taylan Kabbani (Ozyegin University; Huawei Turkey R&D Center); Fatih Enes Usta (Marmara University) |
| Abstract: | Predicting the stock market trend has always been challenging since its movement is affected by many factors. Here, we approach the future trend prediction problem as a machine learning classification problem by creating tomorrow_trend feature as our label to be predicted. Different features are given to help the machine learning model predict the label of a given day; whether it is an uptrend or downtrend, those features are technical indicators generated from the stock's price history. In addition, as financial news plays a vital role in changing the investor's behavior, the overall sentiment score on a given day is created from all news released on that day and added to the model as another feature. Three different machine learning models are tested in Spark (big-data computing platform), Logistic Regression, Random Forest, and Gradient Boosting Machine. Random Forest was the best performing model with a 63.58% test accuracy. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12283&r= |
| By: | Ron Kaniel; Zihan Lin; Markus Pelger; Stijn Van Nieuwerburgh |
| Abstract: | We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum. |
| JEL: | G0 G11 G23 G5 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29723&r= |
| By: | Carmina Fjellstr\"om |
| Abstract: | Performance forecasting is an age-old problem in economics and finance. Recently, developments in machine learning and neural networks have given rise to non-linear time series models that provide modern and promising alternatives to traditional methods of analysis. In this paper, we present an ensemble of independent and parallel long short-term memory (LSTM) neural networks for the prediction of stock price movement. LSTMs have been shown to be especially suited for time series data due to their ability to incorporate past information, while neural network ensembles have been found to reduce variability in results and improve generalization. A binary classification problem based on the median of returns is used, and the ensemble's forecast depends on a threshold value, which is the minimum number of LSTMs required to agree upon the result. The model is applied to the constituents of the smaller, less efficient Stockholm OMX30 instead of other major market indices such as the DJIA and S&P500 commonly found in literature. With a straightforward trading strategy, comparisons with a randomly chosen portfolio and a portfolio containing all the stocks in the index show that the portfolio resulting from the LSTM ensemble provides better average daily returns and higher cumulative returns over time. Moreover, the LSTM portfolio also exhibits less volatility, leading to higher risk-return ratios. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.08218&r= |
| By: | Nikolas Michael; Mihai Cucuringu; Sam Howison |
| Abstract: | We investigate the use of the normalized imbalance between option volumes corresponding to positive and negative market views, as a predictor for directional price movements in the spot market. Via a nonlinear analysis, and using a decomposition of aggregated volumes into five distinct market participant classes, we find strong signs of predictability of excess market overnight returns. The strongest signals come from Market-Maker volumes. Among other findings, we demonstrate that most of the predictability stems from high-implied-volatility option contracts, and that the informational content of put option volumes is greater than that of call options. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09319&r= |
| By: | Meng-Chen Hsieh; Clifford Hurvich; Philippe Soulier |
| Abstract: | We develop and justify methodology to consistently test for long-horizon return predictability based on realized variance. To accomplish this, we propose a parametric transaction-level model for the continuous-time log price process based on a pure jump point process. The model determines the returns and realized variance at any level of aggregation with properties shown to be consistent with the stylized facts in the empirical finance literature. Under our model, the long-memory parameter propagates unchanged from the transaction-level drift to the calendar-time returns and the realized variance, leading endogenously to a balanced predictive regression equation. We propose an asymptotic framework using power-law aggregation in the predictive regression. Within this framework, we propose a hypothesis test for long horizon return predictability which is asymptotically correctly sized and consistent. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.00793&r= |
| By: | Jan Rosenzweig |
| Abstract: | We look at optimal liability-driven portfolios in a family of fat-tailed and extremal risk measures, especially in the context of pension fund and insurance fixed cashflow liability profiles, but also those arising in derivatives books such as delta one books or options books in the presence of stochastic volatilities. In the extremal limit, we recover a new tail risk measure, Extreme Deviation (XD), an extremal risk measure significantly more sensitive to extremal returns than CVaR. Resulting optimal portfolios optimize the return per unit of XD, with portfolio weights consisting of a liability hedging contribution, and a risk contribution seeking to generate positive risk-adjusted return. The resulting allocations are analyzed qualitatively and quantitatively in a number of different limits. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.10846&r= |
| By: | Ivan Letteri; Giuseppe Della Penna; Giovanni De Gasperis; Abeer Dyoub |
| Abstract: | Stock market forecasting is a lucrative field of interest with promising profits but not without its difficulties and for some people could be even causes of failure. Financial markets by their nature are complex, non-linear and chaotic, which implies that accurately predicting the prices of assets that are part of it becomes very complicated. In this paper we propose a stock trading system having as main core the feed-forward deep neural networks (DNN) to predict the price for the next 30 days of open market, of the shares issued by Abercrombie & Fitch Co. (ANF) in the stock market of the New York Stock Exchange (NYSE). The system we have elaborated calculates the most effective technical indicator, applying it to the predictions computed by the DNNs, for generating trades. The results showed an increase in values such as Expectancy Ratio of 2.112% of profitable trades with Sharpe, Sortino, and Calmar Ratios of 2.194, 3.340, and 12.403 respectively. As a verification, we adopted a backtracking simulation module in our system, which maps trades to actual test data consisting of the last 30 days of open market on the ANF asset. Overall, the results were promising bringing a total profit factor of 3.2% in just one month from a very modest budget of $100. This was possible because the system reduced the number of trades by choosing the most effective and efficient trades, saving on commissions and slippage costs. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12286&r= |
| By: | Shi Bo |
| Abstract: | This paper aims to characterize the typical factual characteristics of financial market returns and volatility and address the problem that the tail characteristics of asset returns have been not sufficiently considered, as an attempt to more effectively avoid risks and productively manage stock market risks. Thus, in this paper, the fat-tailed distribution and the leverage effect are introduced into the SV model. Next, the model parameters are estimated through MCMC. Subsequently, the fat-tailed distribution of financial market returns is comprehensively characterized and then incorporated with extreme value theory to fit the tail distribution of standard residuals. Afterward, a new financial risk measurement model is built, which is termed the SV-EVT-VaR-based dynamic model. With the use of daily S&P 500 index and simulated returns, the empirical results are achieved, which reveal that the SV-EVT-based models can outperform other models for out-of-sample data in backtesting and depicting the fat-tailed property of financial returns and leverage effect. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09434&r= |
| By: | Raphael Auer; Bruce Muneaki Iwadate; Andreas Schrimpf; Alexander F. Wagner |
| Abstract: | Although real integration conceptually plays an important role for the comovement of international equity markets, documenting this link empirically has proven challenging. We construct a new dataset of theory-guided, relevant measures of bilateral trade in final and intermediate goods and services. With these measures, we provide evidence of a strong link between changes in real integration – in particular global value chains – and equity market comovement. This also holds when controlling for financial openness and other factors that could confound the role of real openness. These results suggest that supply chain disruptions, for instance due to political tensions and the COVID-19 crisis, might also affect the interconnections between stock markets via rippling through the global production network. |
| Keywords: | financial integration, global value chains, international asset pricing, international trade, real integration, spillovers, stock market comovement, supply chains. |
| JEL: | F10 F36 F65 G10 G12 G15 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1003&r= |
| By: | Kristian Blickle (Federal Reserve Bank of New York); Markus Brunnermeier (Princeton University); Stephan Luck (Federal Reserve Bank of New York) |
| Abstract: | We use the German Crisis of 1931, one of the largest bank runs in financial history, to study how depositors behave in the absence of deposit insurance. We find that banks lose on average around 25% of their overall deposit funding during the run and that there is an equal outflow of retail and non-financial wholesale deposits from both ex-post failing and surviving banks. This implies that regular depositors are unable to identify failing banks. In contrast, the interbank market precisely identifies which banks will fail: the interbank market collapses for failing banks entirely but it continues to function for surviving banks, which can borrow from other banks in response to deposit outflows. We argue that since regular depositors appear uninformed it is unlikely that deposit insurance would exacerbate moral hazard. Instead, interbank depositors are best positioned for providing "discipline" via short-term funding. |
| Keywords: | financial crises, banks, Germany |
| JEL: | G01 G21 N20 N24 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:pri:econom:2021-5&r= |
| By: | Jaydip Sen; Saikat Mondal; Sidra Mehtab |
| Abstract: | 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 LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns. The predicted and the actual returns indicate a very high-level accuracy of the LSTM model. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02723&r= |
| By: | Jaydip Sen; Sidra Mehtab; Abhishek Dutta; Saikat Mondal |
| Abstract: | Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02728&r= |
| By: | Juan Ignacio Pe\~na; Rosa Rodriguez; Silvia Mayoral |
| Abstract: | This paper compares the in-sample and out-of-sample performance of several models for computing the tail risk of one-month and one-year electricity futures contracts traded in the NordPool, French, German, and Spanish markets in 2008-2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk (VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model with Student-t distribution is the best-performing specification with 88% cases in which the Fisher test accepts the model, with a success rate of 94% in the left tail and of 81% in the right tail. The model passes the test of model adequacy in the 100% of the cases in the NordPool and German markets, but only in the 88% and 63% of the cases in the Spanish and French markets. With ES, this model passes the test of model adequacy in 100% of cases in all markets. Historical Simulation and Quantile Regression-based approaches misestimate tail risks. The right-hand tail of the returns is more difficult to model than the left-hand tail and therefore financial regulators and the administrators of futures markets should take these results into account when setting additional regulatory capital requirements and margin account regulations to short positions. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.01732&r= |