Bearish Trend Research Articles

Optimism in Property Markets

House valuation involves forecasting household demand for a neighborhood, whereas commercial property valuation involves forecasting area economic activity. Yet, we show optimistic expectations for both. We investigate further possible disparities due to different cultures of home ownership, short, medium, and long-term investment horizons, and bullish or bearish trends. We use an experimental framework to demonstrate expectations are optimistic under all conditions. Buyers use trends to surmise future prices in bull markets, expect price increases in oscillating markets, and price reversals in bear markets, failing to anticipate price busts. Policy implications are important in preventing property boom-induced financial crises.

The impact of COVID-19 vaccines on China’s overall and sectoral stock returns: a quantile-based analysis

ABSTRACT This research analyzes the response of China’s stock market returns (overall and sector-specific) to COVID-19 vaccines. We examine these relationships using quantile-on-quantile (QQR) regression and causality-in-quantile (CQ) approaches. The empirical results show that the higher quantiles of COVID-19 vaccines propel the higher quantiles of the Shanghai stock returns (SSR). Simultaneously, the higher quantiles of COVID-19 vaccines negatively influence the lower quantiles of the Shanghai stock returns. On the sectoral level, consumer discretionary, basic materials, industrial, utilities, and telecommunication sectors exhibit similar trends to those observed in the Shanghai stock market. The study emphasizes that COVID-19 vaccines tend to increase Shanghai stock returns during bullish market condition, and this impact becomes adverse when the market showcases a bearish trend. These findings serve as insights for policymakers, investors, and other stakeholders.

Behavioral Biases Influencing Individual Investors Decision Making in Bearish Trend at Nepse

The discipline of financial economics has seen a paradigm shift over the past 25 years, moving from traditional finance to a new area of the study known as behavioral finance. Due to a multitude of behavioral biases and emotional attachments, professional investors make bad financial decisions that hinder their investing performance. Due to the increase in individual investors over the past several years, which is predicted to continue in the years to come, the problem of behavioral biases may become more and more important. Overconfidence bias, representativeness bias, disposition bias, loss aversion bias, herding bias and market factors are the independent variables, whereas investment decision making is the dependent variable. Questionnaires were used to collect primary data. 200investors were sampled using the convenience sampling technique, and data obtained was subjected to regression analysis. The SPSS was used to analyze data collected in order to generate descriptive statistics for the study. The extent to which the dependent variables could be explained by the independent variable was described using regression analysis. The study thus suggests that individual investors be educated how to avoid poor investment outcomes caused on by behavioral biases. Furthermore, individual investors should seek the advice of stock brokers/fund managers to guide them accordingly in terms of performance of a specific security in which an investor would wish to invest in.

Harmonizing Macro-Financial Factors and Twitter Sentiment Analysis in Forecasting Stock Market Trends

The surge in generative artificial intelligence technologies, exemplified by systems such as ChatGPT, has sparked widespread interest and discourse prominently observed on social media platforms like Twitter. This paper delves into the inquiry of whether sentiment expressed in tweets discussing advancements in AI can forecast day-to-day fluctuations in stock prices of associated companies. Our investigation involves the analysis of tweets containing hashtags related to ChatGPT within the timeframe of December 2022 to March 2023. Leveraging natural language processing techniques, we extract features, including positive/negative sentiment scores, from the collected tweets. A range of classifier machine learning models, encompassing gradient boosting, decision trees and random forests, are employed to train on tweet sentiments and associated features for the prediction of stock price movements among key companies, such as Microsoft and OpenAI. These models undergo training and testing phases utilizing an empirical dataset gathered during the stipulated timeframe. Our preliminary findings reveal intriguing indications suggesting a plausible correlation between public sentiment reflected in Twitter discussions surrounding ChatGPT and generative AI and the subsequent impact on market valuation and trading activities concerning pertinent companies, gauged through stock prices. This study aims to forecast bullish or bearish trends in the stock market by leveraging sentiment analysis derived from an extensive dataset comprising 500,000 tweets. In conjunction with this sentiment analysis derived from Twitter, we incorporate control variables encompassing macroeconomic indicators, Twitter uncertainty index and stock market data for several prominent companies.

Pre, During and Post Covid-19 Stock Returns and Volatility Comparison:

This study aims to study the pre, during and post-COVID-19 stock returns and volatility associated with Pakistan. For this study, daily financial data from 2018 to 2023. This study is based on daily prices and their returns. The data was divided into categories like Category A: Before the COVID-19 pandemic, Category B: during the COVID-19 pandemic and Category C: Post COVID-19 pandemic. The indices period is from 1st January 2018 to 25th February 2020 (Pre Covid-19), 26th February 2020 to 31st December 2021 (during Covid-19) and from 1st March 2022 to 8th May 2023 (Post Covid-19) period. The data were treated with Descriptive and Econometric Models using E-views, Microsoft Excel and SPSS. The findings of this research are that there is no stationarity issue in all the returns’ series, and the average return for Pre (Category A) and Post Covid periods (Category C) is positive; however, it is negative During the Covid period (Category B) which shows the bearish trend in Pakistan Stock Exchange market (PSX) due to the Covid-19 outbreak. Moreover, the coefficient of variation (CV) is computed, which shows that pre-COVID tenure has the highest relative dispersion (CV = 44.43), followed by during COVID (CV = 40.76), and post-COVID tenure has the most negligible coefficient of relative dispersion (CV = 23.1) which refers to the least volatile phenomenon. The findings also suggest no statistically significant difference among all average returns, and all are equal. The results from the econometric model show that the highest mean reversion value was 0.969991 During the COVID period. Post-COVID has a value of 0.449377, which indicates the quick mean reversion compared to the other two.

A Micro-Level Evidence of how Investor and Manager Herding Behavior Influence the Firm Financial Performance

Using the investor-herding index (IHRB) and manager-herding index (MHRB), the emergence of investors’ and managers’ herding behavior at the corporate-level and its potential impact on firm financial performance (FFP) is analyzed in the entire study. The study utilizes the micro-level data of 296 listed firms of Pakistan stock exchange (PSX) from 2013 to 2017. Empirical findings show that the investors’ and managers’ herding significantly affect FFP. The results are robust under alternative measurements of FFP. This study has a unique contribution to the literature on behavioral finance by addressing the astonishingly overlooked phenomena of firm-level biases and their potential impact on FFP. For prospective investor’s decision, the findings are significant in such way that the market index is irrelevant than the firm-level financial evidence. Explicitly, the study concludes that positive herding behavior is elucidated by the bullish trend and inverse herding behavior of investors with respective to firm value leads toward bearish trend of stock market index. The likelihood of market crash may become more stringent in circumstances once managers and investors interactively exhibit negative herding behavior. Thus, the study offers useful policy and strategy implications for the associated stakeholders to the device the policies accordingly. JEL Code: D9, D21, D91, G11.

Disorder unleashes panic in bitcoin dynamics

The behaviour of Bitcoin owners is reflected in the structure and the number of bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of Bitcoin traders is reflected in the formation of bullish and bearish trends in the crypto market. In light of these observations, we wonder if human behaviour underlies some relationship between the Blockchain and the crypto market. To address this question, we map the Blockchain to a spin-lattice problem, whose configurations form ordered and disordered patterns, representing the behaviour of Bitcoin owners. This novel approach allows us to obtain time series suitable to detect a causal relationship between the dynamics of the Blockchain and market trends of the Bitcoin and to find that disordered patterns in the Blockchain precede Bitcoin panic selling. Our results suggest that human behaviour underlying Blockchain evolution and the crypto market brings out a fascinating connection between disorder and panic in Bitcoin dynamics.

Housing market forecasts via stock market indicators

Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.

An algorithmic trading system based on a stacked generalization model and hidden Markov model in the foreign exchange market

The Forex market has been one of the most attractive markets to researchers, funds, and traders. Literature shows that a single model algorithm usually cannot perform satisfactorily on the foreign exchange (Forex) time series because of the market's complexity. This study develops an algorithm based on two stacked generalization models consisting of four machine-learning models. First, the optimal lags of features are found using the Fisher discriminant ratio and partial autocorrelation function. Second, one stacked model fits the bullish trends, and the other holds the bearish trends resulting from a hidden Markov model. We reinforce the predictive signals of these models by extracting relationships between currency pairs with correlation and mutual information. Lastly, the proposed algorithm constructs a portfolio based on the strength of signals dependent on correlation and mutual information. As a result, this paper compares the performance of the proposed approach with different states of the model and several established benchmarks regarding return and risk metrics. The outcomes show that the proposed model's added features—such as time series clustering, considering a range of inputs, and internal relationships among different assets—can increase its performance in the Forex market.

KOMPARASI REKSADANA SAHAM KONVENSIONAL DENGAN REKSADANA SAHAM SYARIAH

This study aims to determine whether there is a difference between the performance of Islamic mutual funds and conventional mutual funds in bullish and bearish market trends. The analytical method used is the mean difference test uses the independent sample t-test method, through the SPSS program by comparing the Jensen and Treynor indices. The samples of this study were 15 conventional mutual funds and 15 sharia mutual funds based on the highest achieved NAV. in each period 2017 as a bullish trend period and the 2020 period as a bearish trend period.
 Based on the Jensen index by Independent sample t-test in 2017 (bullish trend), there is a significant difference between the performance of conventional stock mutual funds and the performance of Islamic stock mutual funds and the performance of conventional stock mutual funds is better than Sharia, while in the 2020 (Bearish trend) there is no differene between performance of conventional equity mutual funds and performance of Islamic equity mutual funds. Based on the Treynor index by independent sample t-test in both bullish and bearish periods, there is no difference between the performance of conventional equity funds and the performance of Islamic equity funds

Reputational contagion and the fall of FTX: Examining the response of tokens to the delegitimization of FTT

Intertwined with the recent fall of cryptocurrency exchange FTX was the unveiling to investors of the lack of asset support for FTX's flagship token FTT. Given interest in reputational contagion, as well as recent academic and investor interest in digital tokens, we are motivated to take advantage of the FTX/FTT event to investigate reputational contagion to other tokens. We employ an event study approach to gauge the impact of the FTX collapse on a variety of tokens. Results of abnormal returns, as well as CARs and CAARs, indicate almost all tokens initiated a persistent bearish trend on the event day.

Covid-19 Induced Herding: A Case of Sectoral Indices of The Indian Stock Market

Herding tendencies are likely to be more prominent during the crisis period. The Covid-19 outbreak has been nothing less than a crisis for the world. Assuming the possibility of Covid-19 induced herding among investors, this study attempts to examine the behaviour in the Indian stock market (S&P BSE) by analyzing the daily returns data of 10 indices and the S&P BSE Sensex using the CSAD model from January 01, 2015 to December 31, 2020, by dividing the period into three sets: whole period, pre Covid-19 outbreak period and during Covid-19 outbreak period. The complete analysis is done for three different states of the markets (general – overall market condition, bullish and bearish). The study finds the Indian stock market to experience sector specific herding behavior during Covid-19 period under the study, in general, as well as during bullish and bearish trends.

Safe haven properties of green, Islamic, and crypto assets and investor's proclivity towards treasury and gold

Turbulent times significantly exacerbate the search for a safe haven asset, inspired by investors' loss aversion. The same happened during the Covid-19 crisis when recurring losses forced investors to alter their investment strategies, and the search for alternative investment classes picked momentum. This study evaluates the safe haven properties of Green financial products, Islamic assets, and Cryptocurrencies, all of which gained prominence in financial markets after the global financial crisis, coupled with the long-acknowledged safe haven assets like Gold, Silver, and Treasuries. We employ a quantile VAR framework to examine the connectedness between the assets' markets during stressed, normal, and euphoric periods. Our results show that both Green and Islamic Bonds only act as a safe haven during the normal market condition; however, US Treasury, cryptocurrencies, and gold emerged as safe-haven assets under bearish or extreme volatility periods. While proclivity towards US Treasury and gold supports the phenomenon of flight-to-safety, we find cryptos have also become investors' preference amid bearish trends to satiate their risk appetite finding their way into the list of speculative assets, if not the true safe havens for investors.

Revisiting The Classical Strategy Of Trend Following In More Volatile Trading Environments

Trend-following strategies (TFS) have been well-established for their effectiveness in analysing stock prices for decades. However, there remains a pressing need to revisit and analyse their performance in today’s increasingly volatile financial environment. First, this study investigated their profitabilities with respect to the S&P500 fund over the past 10 years. The fund’s consistent and strong uptrend over the 10-year period resulted in TFS being unable to outperform the passive buy-and-hold strategy. Longer moving averages and breakout lengths were more profitable given the fund’s bullish nature. Additionally, it was found that exponential moving averages were more effective than simple moving averages. The study also established that trading more frequently, such as daily, had no advantage over trading weekly or monthly. TFS incorporated with stop losses were largely ineffective and were only profitable when market prices displayed strong and consistent trends. Second, this study examined the relevance of TFS in varying economic climates by using data across various market sectors and time periods. It was found that TFS performed better when prices display both bullish and bearish trends as opposed to when prices only trend in one direction or experience frequent fluctuations. Given the steady uptrend in the S&P500 fund in recent times, the effectiveness of these strategies have deteriorated compared to the past where price patterns were less consistent. Thus, it can be said that the relevance of TFS have diminished for funds displaying consistent one-directional trends, like the S&P500 fund, or extremely volatile price patterns.

Stock Market Volatility and the COVID-19 Pandemic in Emerging and Developed Countries: An Application of the Asymmetric Exponential GARCH Model

The objective of this research is to analyze the influence of COVID-19 on the return and volatility of stock market indices of emerging and developed countries (Brazil, Canada, United States, France, India, and Mexico) using an asymmetric exponential GARCH model. The daily returns of the market indices from January 2019 to December 2020 were considered. The results reveal negative average daily returns for all stock market indices during the first period of the COVID-19 pandemic (January 2020 to June 2020). Although the second half of the pandemic period (2020) reflects a recovery of all indices with altered strengths, volatility remains higher than in normal periods, signaling a bearish trend in the market. The variable COVID-19 has been shown to have a positive impact on the volatility of stock returns for all indices, i.e., indicating increased volatility in the analyzed stock markets. In addition, it is also found that the COVID variable has a negative impact on average returns only in the stock market of Brazil and France.

Do geopolitical oil price risk, global macroeconomic fundamentals relate Islamic and conventional stock market? Empirical evidence from QARDL approach

This study examines how the geopolitical oil price risk index, global gold price, global interest rate, and global exchange rate affect Islamic and conventional securities. For empirical estimation, this study has applied Quantile Autoregressive Distributive lag (QARDL) method on the monthly return of the Dow Jones conventional stock market index and Dow Jones Islamic Market World Index from January 2000 to November 2020. The primary variable of concern, geopolitical oil price risk, shows significant results for both securities under the bullish situation. Furthermore, under a bearish trend, the global exchange rate and the global interest rate of the Islamic stock market act in a similar direction. Although in conventional securities, both variables are significant in a bullish trend. The findings of this paper are important for investors and policymakers because this study provides a clear and comprehensive picture to shareholders in terms of their investments in Islamic or traditional markets. Moreover, it will open new opportunities for portfolio managers and speculators in Islamic and conventional stock markets.

The impacts of COVID-19 crisis on spillovers between the oil and stock markets: Evidence from the largest oil importers and exporters

This study examines the multiscale spillovers and nonlinear causalities between the crude oil futures market and the stock markets of the United States (US), Canada, China, Russia, and Venezuela before and during the COVID-19 pandemic. Using the wavelet coherency method, we find strong co-movement between the oil futures market and these five stock markets, particularly from March 2020 to May 2020 (initial period of the COVID-19 outbreak) at high frequency. Furthermore, we find positive co-movements at low frequency during the overall COVID-19 period. This finding suggests that the bearish trend of stock markets is associated with a downward movement in oil prices. Using the wavelet-based Granger causality approach, we find that the oil and stock indices have less co-movement on a smaller scale but greater movement on a larger scale across all periods. As an exception, the Russian market is significantly influenced by oil prices, even on a small scale, before the COVID-19 period, but not after the beginning of the pandemic. We also find effects in the opposite direction—the Canadian and U.S. markets influence oil prices on a small scale during the COVID-19 period, an effect that is not visible for the U.S. market in the pre-COVID-19 sample. The results also show a significant bidirectional causality from oil to stock markets and vice versa during Russian-Saudi oil price war at high scale. Furthermore, we find that investors should hold more oil futures than stock shares in their portfolios for all periods. This evidence confirms that oil instruments are important for hedging during normal periods and act as safe-haven assets during crisis periods. We observe that the U.S. and Canadian stock markets were more affected by oil price shocks than were other countries.

Economic Scar Tissue of COVID-19 Puzzle: An Analysis, Evidence and Suggestion on Global Perspective

The COVID-19 pandemic has instigated tremendous human and economic hardship around the world. Using meta-literature and time series analysis, we conduct both synthesis and empirical analysis to investigate particularly the economic perspectives of COVID-19 across several financial systems: (a) Asian market, (b) European market, (c) American market and (d) Gulf Cooperation Council (GCC) and Middle East and North Africa’s (MENA) market. The critical review of the leading business and finance journals of ISI-WOS summarizes that the outburst of COVID-19 mercilessly affects global economies; however, the end phase of the systematic cascading effect has not clearly folded yet. The probable reasons of economic downturn are productivity reduction, labour immobility, undue job loss, scarcity of employment opportunities, discontinuation of supply chain, declining foreign exports, investment uncertainty, adverse clientele effect, etc. However, after analysing the pre- and during COVID effect on foreign reserve and remittance, we identify an inconclusive finding: (a) bullish trend, (e.g., the USA, Canada, Mexico, Japan, India, Bangladesh and Singapore); (b) bearish trend, (e.g., the UK, Sri-Lanka, Saudi Arabia, Malaysia, Nigeria, Italy and Brazil). Our time series analysis between pre- and during COVID-19 also documents the economic mystery that although the overall economic growth has gone down, foreign reserve and remittance have increased gradually across several economies. Overall, the current global situation demands systematic, well-targeted and aggressive fiscal-monetary stimulus initiatives. Therefore, this study offers theoretical, empirical and policy-oriented academic novelty with the possible suggestions and dynamic strategies to circumvent COVID-19 adverse effects.

Predicting Bitcoin Return Using Extreme Value Theory

The study investigates and develops the ability of the extreme value theory (EVT) to predict bitcoin return. EVT is used to deal with rare but extreme events, such as severe losses or excessive damages. It is being used as a powerful statistical tool in various disciplines, including finance, engineering, environmental science, and actuarial science. As the largest among all cryptocurrencies in existence, bitcoin’s behavior is primarily characterized by great volatility. Predicting bitcoin return is complex and important, primarily because of the extreme nature of its return. There is not enough substantial research involving EVT in bitcoin analysis. This study has three objectives. First, confirming the extreme nature of bitcoin return by various statistical tests; second, modeling the bitcoin return using two different EVT approaches (block maxima approach and peak over threshold approach); and third, assessing uncertainties by predicting bitcoin return levels for 5-, 10-, 20-, 50-, and 100-years with a 95% confidence interval using both of these methods. These results could certainly serve policymakers and investors, as these return levels can be useful in characterizing bearish and bullish trends and predicting the same. Moreover, these can serve as starting points for future studies regarding the stationary and non-stationary properties of bitcoin return.

NEPSE in Bollinger Bands

<abstract> <p>An investor uses the graphical presentation of Bollinger Bands to get signals of the ups and downs, as well the volatility of the market from the expansion and tightening of the UBB and LBB, reflecting higher and lower volatility. The percent (%) b helps determine the opportunities during extreme periods from the market, looking at the concentration of line graph at the value "0" or "1" reflecting the bearish and bullish trend, respectively. The Bandwidth Index was able to picture out the bullish trend with a squeeze at the upper band. The positive unimodality of Q for NEPSE daily return for the period of the fiscal year 1998–1999 to the fiscal year 2019–2020 indicated normality for the market return. Nevertheless, the results for the trading signals based on the Bollinger bands are seen as useful for an investor by giving a clear signal to "buy" or "sell". At the same time, relying only on Bollinger Bands with a specific period MA, i.e. the Bollinger Bands with a shorter moving average (MA) shows higher fluctuations and vice-versa, hence, could show false signals while choosing inappropriate MA, therefore, help of other technical analysis tools should be taken while going for an investment decision.</p> </abstract>