Effects of COVID-Induced Public Anxiety on European Stock Markets: Evidence From a Fear-Based Algorithmic Trading System

The effect of COVID-induced public anxiety on stock markets, particularly in European stock market returns, is examined in this research. The search volumes for the notion of COVID-19 gathered by Google Trends and Wikipedia were used as proxies for COVID-induced public anxiety. COVID-induced public anxiety was shown to be linked with negative returns in European stock markets when a panel data method was used to a sample of data from 14 European stock markets from January 2, 2020 to September 17, 2020. Using an automated trading system, we used this finding to suggest investment methods based on COVID-induced anxiety. The findings of back-testing indicate that these techniques have the potential to generate exceptional profits. These results have significant consequences for government officials, the media, and investors.

Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances

We revisit Markowitz’s mean-variance portfolio selection model by considering a distributionally robust version, in which the region of distributional uncertainty is around the empirical measure and the discrepancy between probability measures is dictated by the Wasserstein distance. We reduce this problem into an empirical variance minimization problem with an additional regularization term. Moreover, we extend the recently developed inference methodology to our setting in order to select the size of the distributional uncertainty as well as the associated robust target return rate in a data-driven way. Finally, we report extensive back-testing results on S&P 500 that compare the performance of our model with those of several well-known models including the Fama–French and Black–Litterman models. This paper was accepted by David Simchi-Levi, finance.

Performance Analysis of Investment Portfolio Strategy Using Warren Buffett, Benjamin Graham, and Peter Lynch Method in Indonesia Stock Exchange

Warren Buffett, Benjamin Graham, and Peter Lynch are three (3) famous investors’ gurus in the world that have already proved that they can outperform the market by value investing method. Method that they are using are based on fundamental analysis and they screen the company’s stock based on several key financial ratios and criteria that they found important in analyzing the company. In this project, Author conducted research and study to find out the applicability of the screening method made by the gurus in Indonesia Stock Exchange (IDX) using equally weighted method, back testing it in May 2012 until December 2020 periods, and evaluate the performance of each type of portfolios made using Sharpe ratio, Treynor ratio, and Jensen’s alpha. The result of this project is all type of these portfolios are having positive risk adjusted returns. Peter Lynch type of portfolio is having the highest annualized return 24.04 % or 613 % cumulative return, while Warren Buffett and Benjamin Graham are having annualized returns 9.42 % (or cumulative return 216.48%) and 8.3 % (or cumulative return 198.27%) respectively. Moreover, Author found that those three types of portfolios are having beta (β) nearly the same with one (1) means that the portfolios are having same risk with its systematic (market) risk.

Implementation of Fibonacci Retracements and Exponential Moving Average (EMA) Trading Strategy in Indonesia Stock Exchange

Based on KSEI statistic data on March 2021, IDX individual stock market investor is increasing 199% compared to 2018 becoming 4,848,954 number of investors. 56.9% population of the individual investor is having ages that less than 30 years. In the period where IDX was bullish in November 2020 - January 2021, there is a phenomenon where stocks influencers appeared in social media and impacted to the stock price movement after the announcement is done by the influencer. In contrary, during bearish and sideways condition, those influencers were gone and changed with bad news that went viral where many individual investors are lost their capital in IDX. They lose money since they are gambling in the stock market without any analysis and no establishment of trading plan. This research is aimed as a strategy to individual investors in IDX to implement trading strategy based on Fibonacci retracements and projections, EMA lines, trendlines, stochastic, and volume. Back testing is conducted in IDX SMC Liquid index constituents during January 2018 until December 2020 period. By implementing this trading strategy, return generated is 164% for 3 years trading time frame. Author also found that this trading strategy is effective in bullish trend condition especially for individual investors that have long position.

Basel violations, volatility model variants and value at risk: Optimization of performance deviations in banks

Basel penalties originate from VaR violations where a bank may end up either holding more capital or will risk to be reverted to standardized approach. Regulatory capital charge can have a huge impact on banks’ profitability which depends on the estimation of VaR thresholds which is evaluated by the approaches like hypothesis tests, back-testing procedures and Basel Accord regulatory calculations for penalty zones are used. A multi-criteria performance measure has been introduced in this study in order to select the optimal internal model based on performance evaluation techniques which could possibly help in reduction in the VaR violations and thus may leave more capital with banks.

The Global Interest in Vaccines and Its Prediction and Perspectives in the Era of COVID-19. Real-Time Surveillance Using Google Trends

Background: The COVID-19 pandemic has globally overwhelmed all sectors of life. The fast development of vaccines against COVID-19 has had a significant impact on the course of the pandemic. Methods: Global data from Google Trends was analyzed for vaccines against flu, BCG, HPV, pneumococcal disease, polio, and COVID-19. The time frame includes the last five-year period starting from 17 April 2016. Multiple training of time series models with back testing, including Holt–Winters forecasting, Exponential Smoothing State Space, Linear model with trend and seasonal components (tlsm), and ARIMA was conducted. Forecasting according to the best fitting model was performed. Results: Correlation analysis did not reveal a decrease in interest in vaccines during the analyzed period. The prediction models provided a short-term forecast of the dynamics of interest for flu, HPV, pneumococcal and polio vaccines with 5–10% growth in interest for the first quarter of 2022 when compared to the same quarter of 2021. Conclusions: Despite the huge interest in the COVID-19 vaccine, there has not been a detectable decline in the overall interest in the five analyzed vaccines.

Predicting the trend of stock index based on feature engineering and CatBoost model

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.