Stock Indices Breakdown during the Pandemic as the Most Dynamic Bear Market in History: Consequences for Individual Investors

The breakdown of stock indices is an obvious part of the financial market cycle. A common question about a bear market is the time and the depth of the downtrend, as well as the speed of the following recovery. As the COVID-19 pandemic spread globally, it induced huge price drops in a very short period, and an uptrend with new historical highs afterwards. The results of this research show that the pandemic breakdown was the fastest bear market in history; however, it does not confirm that future downtrends will be at the same or even greater speed. The consequences for individual investors have forced them to prepare for possible similar market behavior in the future, and to adjust their trading techniques and strategies to these conditions.

The dynamics of cryptocurrency market behavior: sentiment analysis using Markov chains

PurposeThe authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.Design/methodology/approachThe authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.FindingsThe authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.Originality/valueThe proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.

Optimal Selling Strategies under Regime-Switching Market Environment with Finite Expiry

This paper addresses an optimal stock liquidation problem over a finite-time horizon; to that end, we model it as an optimal stopping problem in a regime-switching market. The optimal stopping time is written as a solution to a system of Volterra type integral equations. Moreover, it reveals that when the risk-free interest rate is always lower than the return rate of the stock, it is never optimal to sell the stock early; otherwise, one should sell the stock in bear market if the stock price reaches a critical value and hold the stock in bull market until the maturity date. Finally, we present a trinomial tree method for numerical implementation. The numerical results are consistent with the theoretical findings.

Market Trends and Options Trading: Viewpoint, Probability and Implications

In Dow theory, market trends are classified as secular trends for long-term frames, primary trends for medium-term frames, and secondary trends for short-term frames. For the long and medium terms, they can consist of major bull (bear) markets and minor bear (bull) markets; for the short terms, they may have corrections and bear rallies. These definitions of market trends are not very helpful to options traders because in practice, options trading is often done on a short-term time frame and options have a unique property of time value. Even in a bull market, there is a possibility of losing all money for buying call options; in a bear market, there is a probability of earning money for buying call options. This inconsistency often troubles options traders deeply. From the viewpoint of options trading, we introduce a new concept of analyzing the market trends and propose new methods for estimating the probabilities of the market trends since at any time the future prices are unknown. By simplifying the market trends into three concepts of uptrend, downtrend, and neutral trend, it will have consistent implications for options trading. JEL classification numbers: C13, G10, G13. Keywords: Market trends, Options trading, Trend probability, Estimation methods, Trading implications.

Safe-haven assets for U.S. equities during the 2020 COVID-19 bear market

The recent stock market downturn is differentiated from the previous ones as it is due to an economic, rather than a financial occurrence (the COVID-19 Pandemic). The purpose of our study is to examine gold, bitcoin, and U.S. Treasury bonds as a safe haven during the COVID-19 bear market. Unlike many studies that support gold as a traditional safe haven for stocks, our study finds that bitcoin and Treasury bonds perform better as a safe haven than gold during the recent COVID-19 bear market.

Liquidity Risk and Asset Pricing in Pakistan Stock Exchange

This paper empirically investigates the impact of liquidity risk on stock returns in Pakistan and determines investors' attitude under bull and bear market conditions. Specifically, the liquidity adjusted capital asset pricing model(CAPM) is modified by including the interaction between the liquidity risk and the indicators of bull- and bear-market periods to investigate whether the pricing of liquidity risk differs in both upward and downward market trends. The analysis is carried out for a large panel of Pakistani manufacturing firms listed at the Pakistan Stock Exchange for the period January 2000 – December 2015. We use alternative liquidity risk measures to check the robustness of the liquidity risk effect. We observe that higher liquidity risk yields higher excess stock returns, implying pricing of liquidity risk during the examined period. The results also reveal that the liquidity risk is positively and significantly related to excess returns in the high-liquidity-risk beta portfolios, whereas it is negatively or insignificantly related to excess returns of low-liquidity-risk beta portfolios. The results also provide evidence that stocks affected by liquidity risk yield positive expected returns in both bull and bear market conditions. However, we find significant differences in the pricing of liquidity risk under upward and downward market trends. The robustness check confirms that the findings on the pricing of liquidity risk are not driven by any specific measure of liquidity.

Premium Valuation of the Pension Benefit Guaranty Corporation with Regime Switching

The Pension Benefit Guaranty Corporation (PBGC) provides insurance coverage for single-employer and multiemployer pension plans in private sector. It has played an important role in protecting the retirement security for over 1.5 million people since it was established about half a decade ago. PBGC collects insurance premiums from employers that sponsor insured pension plans for its coverage and receives funds from pension plans that it takes over. To address the issue of underfunded plans that the PBGC has, this work studies how to evaluate risk-based premiums for the PBGC. Inspired by a couple of existing work in which the premature termination of pension fund and distress termination of sponsor assets are analyzed separately, our work examines the two types of terminations under one framework and considers the occurrence of each termination dynamically. Given that market regime might have a big impact on the dynamics of both pension fund and sponsor’s assets, we thus formulate our model using a continuous-time two-state Markov chain in which bull market and bear market are delineated. We thus formulate our model using a continuous-time two-state Markov Chain in which bull market and bear market are delineated. In other words, the pension fund and sponsor assets are market dependent in our work. Given that this additional uncertainty described by regime switching makes the market incomplete, we therefore utilize the Esscher transform to determine an equivalent martingale measure and apply the risk neutral pricing method to obtain the closed-form expressions for premium of PBGC. In addition, we carry out numerical analysis to demonstrate our results and observe that premium increases according to the retirement benefit irrespective of the type of terminations. In comparison to the case of early distress termination of sponsor assets, the premium goes up more quickly when premature termination of pension funds occurs first due to the fact that pension fund is the first venue of retirement security. Furthermore, we look at how the premium changes with respect to other key parameters as well and make some detailed observations in the section of numerical analysis.