The US 2000‐2002 market descent: How much longer and deeper?

Abstract

A remarkable similarity in the behaviour of the US S&P500 index from 1996 to August 2002 and of the Japanese Nikkei index from 1985 to 1992 (11 year shift) is presented, with particular emphasis on the structure of the bearish phases. Extending a previous analysis of Johansen and Sornette on the Nikkei index ‘antibubble’ based on a theory of cooperative herding and imitation working both in bullish as well as in bearish regimes, we demonstrate the existence of a clear signature of herding in the decay of the S&P500 index since August 2000 with high statistical significance, in the form of strong log‐periodic components. In the next two years, we predict an overall continuation of the bearish phase, punctuated by local rallies; we predict an overall increasing market until the end of the year 2002 or until the first quarter of 2003; we predict a severe following descent (with maybe one or two severe ups and downs in the middle) which stops during the first semester of 2004. Beyond this, we cannot be very certain due to the possible effect of additional nonlinear collective effects and of a real departure from the antibubble regime. The similarities between the two stock market indices may reflect deeper similarities between the fundamentals of the two economies which both went through over‐valuation with strong speculative phases preceding the transition to bearish phases characterized by a surprising number of bad surprises (bad loans for Japan and accounting frauds for the US) sapping investors’ confidence.     

Option pricing model with sentiment

In this paper, we develop a closed-form option pricing model with the stock sentiment and option sentiment. First, the model shows that the price of call option is amplified by bullish stock sentiment, and is reduced by stock bearish sentiment, and the price of put option is in the opposite situation. Second, the price of call option is more sensitive to bullish stock sentiment; the price of put option is more sensitive to bearish stock sentiment. Third, the price of call option increases substantially with respect to the stock sentiment and the option sentiment. The price of put option decreases substantially with respect to the stock sentiment, increases substantially with respect to the option sentiment. Fourth, our models also reveal that the option volatility smile is steeper (flatter) when the stock sentiment becomes more bearish (bullish). Finally, stock sentiment and option sentiment lead to the option price deviating from the rational price. The model could offer a partial explanation of some option anomalies: option price bubbles and option volatility smile.     

Do Opinions on Financial Misstatement Firms Affect Analysts’ Reputation with Investors? Evidence from Reputational Spillovers

We examine whether opinions on firms subsequently revealed to have misstated earnings affect analysts’ reputation with investors. We find that positive opinions by bullish analysts hurt their reputation, leading investors to react less to their research on non‐misstatement firms after the misstatement revelation (i.e., negative spillovers). We also find that bearish analysts issuing more negative opinions gain reputation and experience positive spillovers. Finally, for analysts who dropped coverage of the misstatement firm before the misstatement revelation, we find no spillovers, which suggests that analysts experience limited reputational gains when they did not issue a public negative opinion.     

Short note: Stein's two-stage procedure for the intra-class model

In his nice paper (Mykhopadhyay, 1982) as well as in his significant monograph (Mykhopadhyay & Solanky, 1994) N. Mykhopadhyay considers the following application of STEIN's two-stage procedure: Suppose that (X ₁,..., X_n ) ^T , n = 1, 2,..., is n-dimensional normal with mean vector µ = µ l and dispersion matrix Σ _n =σ ²(ρ_ij ) with ρ_ij = 1, ρ_ij = ρ ^*, i ≠ j = 1,..., n where (µ, Σ, ρ) ∈ ? × ?⁺ × (-1, 0); this is called the intra-class model. For given d > 0 and α ∈ (0, 1) one wants to construct a (sequential) confidence interval I for µ having width 2d and confidence coefficient at least (1 - α). It is claimed that where N is determined, according to Stein's two-stage procedure (Stein, 1945), as where m ? 2 is the first stage sample size and denotes the sample variance, fulfills this aim.     

Forecasting market turbulence using regime-switching models

We propose an early warning system to timely forecast turbulence in the US stock market. In a first step, a Markov-switching model with two regimes (a calm market and a turbulent market) is developed. Based on the time series of the monthly returns of the S&P 500 price index, the corresponding filtered probabilities are successively estimated. In a second step, the turbulent phase of the model is further specified to distinguish between bullish and bearish trends. For comparison only, a Markov-switching model with three states (a calm market, a turbulent bullish market, and a turbulent bearish market) is examined as well. In a third step, logistic regression models are employed to forecast the filtered probabilities provided by the Markov-switching models. A major advantage of the presented modeling framework is the timely identification of the factors driving the different phases of the capital market. In a fourth step, the early warning system is applied to an asset management case study. The results show that explicit consideration of the models’ signals yields better portfolio performance and lower portfolio risk compared to standard buy-and-hold and constant proportion portfolio insurance strategies.     

A Better Asymmetric Model of Changing Volatility in Stock and Exchange Rate Returns: Trend-GARCH

Abstract

The impact of short run price trending on the conditional volatility is tested empirically. A new family of conditionally heteroscedastic models with a trend-dependent conditional variance equation: The Trend-GARCH model is described. Modern microeconomic theory often suggests the connection between the past behaviour of time series, the subsequent reaction of market individuals, and thereon changes in the future characteristics of the time series. Results reveal important properties of these models, which are consistent with stylized facts found in financial data sets. They can also be employed for model identification, estimation, and testing. The empirical analysis supports the existence of trend effects. The Trend-GARCH model proves to be superior to alternative models such as EGARCH, AGARCH, TGARCH OR GARCH-in-Mean in replicating the leverage effect in the conditional variance, in fitting the news impact curve and in fitting the volatility estimates from high frequency data. In addition, we show that the leverage effect is dependent on the current trend, i.e. it differentiates between bullish and bearish markets. Furthermore, trend effects can account for a significant part of the long memory property of asset price volatilities.     

Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S.

In this paper, we propose a novel approach to examine the risk spillovers between FinTech firms and traditional financial institutions, during a time of fast technological advances. Based on the stock returns of U.S. financial and FinTech institutions, we estimate pairwise risk spillovers by using the Granger causality test across quantiles. We consider the whole distribution: the left tail (bearish case), the right tail (bullish case) and the center of the distribution and construct three types of spillover networks (downside-to-downside, upside-to-upside, and center-to-center) and obtain network-based spillover indicators. We find that linkages in the network are stronger in the bearish case when the risk of spillover is higher. FinTech institutions' risk spillover to financial institutions positively correlates with financial institutions' increase in systemic risk. These results have important policy implications, as they underscore the importance of enhancing the supervision and regulation of FinTech companies, to maintain financial stability.     

Investor Sentiment and Option Prices

This paper examines whether investor sentiment about the stockmarket affects prices of the S&P 500 options. The findingsreveal that the index option volatility smile is steeper (flatter)and the risk-neutral skewness of monthly index return is more(less) negative when market sentiment becomes more bearish (bullish).These significant relations are robust and become stronger whenthere are more impediments to arbitrage in index options. Theycannot be explained by rational perfect-market-based optionpricing models. Changes in investor sentiment help explain timevariation in the slope of index option smile and risk-neutralskewness beyond factors suggested by the current models.     

Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis

This paper examines the quantile dependence, connectedness, and return spillovers between gold and the price returns of leading cryptocurrencies, using quantile cross-spectral, the return spillovers based the quantile VAR, and quantile connectedness approaches. The results show that the dependencies within cryptocurrencies are highly symmetric and sensitive to different quantile arrangements. Under normal market conditions, we find a high positive dependence within cryptocurrencies and a low positive dependence between cryptocurrencies and gold. The dependence is higher at long term than intermediate- and short- terms before the pandemic during bearish market conditions. In contrast, the degree of dependence decreases at the intermediate- and long-terms during COVID-19 period than before. Moreover, the magnitude of return spillovers is higher at lower quantile (bearish market) than upper quantile (bullish market). Gold serves as a safe haven and diversifier asset for cryptocurrencies during COVID-19 outbreak at both intermediate and long terms.     

The extreme risk connectedness of the new financial system: European evidence

The paper empirically analyses the tail risk connectedness between FinTech and the banking sector in the European context over 2015–2022. For this purpose, we use the Tail-Event driven NETworks (TENET) risk model, i.e., we can capture the behaviour of extreme (negative and positive) risk spillover within the financial system. The results highlight how most tail risk spillovers are from banks to FinTech firms. Also, the findings suggest that the spillovers of cross-sector tail risk are more significant in downside (bearish) risk conditions than in upside (bullish) one. We find evidence of an asymmetric effect of extreme risk spillover to the real economy. Finally, we evaluate the monetary policy’s impact on extreme risk. Our findings highlight the importance of closer monitoring risk spillover between FinTech institutions and the European banking system to maintain financial stability.     

Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms

The behaviourally based portfolio selection problem with investor’s loss aversion and risk aversion biases in portfolio choice under uncertainty is studied. The main results of this work are: developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical comparative analysis of this model and the index tracking model. The crucial assumption is that behavioural features of the prospect theory model provide better downside protection than traditional approaches to the portfolio selection problem. In this research the large-scale computational results for the prospect theory model have been obtained for real financial market data with up to 225 assets. Previously, as far as we are aware, only small laboratory tests (2–3 artificial assets) have been presented in the literature. In order to investigate empirically the performance of the behaviourally based model, a differential evolution algorithm and a genetic algorithm which are capable of dealing with a large universe of assets have been developed. Specific breeding and mutation, as well as normalization, have been implemented in the algorithms. A tabulated comparative analysis of the algorithms’ parameter choice is presented. The prospect theory model with the reference point being the index is compared to the index tracking model. A cardinality constraint has been implemented to the basic index tracking and the prospect theory models. The portfolio diversification benefit has been found. The aggressive behaviour in terms of returns of the prospect theory model with the reference point being the index leads to better performance of this model in a bullish market. However, it performed worse in a bearish market than the index tracking model. A tabulated comparative analysis of the performance of the two studied models is provided in this paper for in-sample and out-of-sample tests. The performance of the studied models has been tested out-of-sample in different conditions using simulation of the distribution of a growing market and simulation of the t-distribution with fat tails which characterises the dynamics of a decreasing or crisis market.     

A new parametric model for converting excess mortality from clinical studies to insured population

We propose a new parametric model – the generalized excess mortality (GEM) model – for converting excess mortality from clinical to insured population. The GEM model has been formulated as a generalization of the excess death rate (EDR) model in terms of a single adjustment parameter (m) that accounts for a partial elimination of a clinical study’s EDR due to the underwriting selection process. The suggested value of the parameter m depends only on the ratio of the impairment’s prevalence rate in the insured population to that in the clinical population. The model’s development has been implemented in two phases: the design phase and the validation phase. In the design phase, the data from the National Health and Nutrition Examination Survey I pertaining to three broad impairments (diabetes, coronary artery disease, and asthma) have been used. As a result, the following equation for the parameter m has been proposed: m_k?=?(P_i,k/P_c,k)ⁿ, where P_i,k, P_c,k are the prevalence rates of impairment k under study in the insured and the clinical populations, respectively, and n a single universal parameter with its value best approximated as n?=?0.5 (95% confidence interval 0.5–0.6). In the validation phase, several independent clinical studies of three other impairments (Crohn’s disease, epilepsy, and chronic obstructive pulmonary disease) were used. As it has been demonstrated in the validation phase, for a number of impairments, the GEM model can provide a better fit for observed insured population mortality than either one of the conventional EDR or mortality ratio models.     

Stock market volatility, excess returns, and the role of investor sentiment

Using the Investors' Intelligence sentiment index, we employ a generalized autoregressive conditional heteroscedasticity-in-mean specification to test the impact of noise trader risk on both the formation of conditional volatility and expected return as suggested by De Long et al. [Journal of Political Economy 98 (1990) 703]. Our empirical results show that sentiment is a systematic risk that is priced. Excess returns are contemporaneously positively correlated with shifts in sentiment. Moreover, the magnitude of bullish (bearish) changes in sentiment leads to downward (upward) revisions in volatility and higher (lower) future excess returns.     

The Sophisticated and the Simple: the Profitability of Contrarian Strategies

A variety of variables have been used to form contrarian portfolios, ranging from relatively simple measures, like book‐to‐market, cash flow‐to‐price, earnings‐to‐price and past returns, to more sophisticated measures based on the Ohlson model and residual income model (RIM). This paper investigates whether: (i) contrarian strategies based on RIM perform better or worse than those based on the Ohlson model; (ii) contrarian strategies based on more sophisticated valuation models (e.g. Ohlson and RIM) perform much better than the relatively simpler ranking variables that have been used so extensively in the finance literature. Given that the RIM and Ohlson models require greater information inputs and technical know‐how, and make different implicit assumptions on future abnormal earnings, it is important to ascertain if they offer significantly greater contrarian profits to outweigh the increased costs that they entail. Indeed, our surprising finding is that simple cash flow‐to‐price measures appear to do almost as well as the more sophisticated alternatives. One would have expected the sophisticated models to significantly outperform the simple cash flow to price model for the reasons given by Penman (2007) .     

Noise trading and stock market volatility

We investigate the relative effects of fundamental and noise trading on the formation of conditional volatility. We find significant positive (negative) effects of investor sentiments on stock returns (volatilities) for both individual and institutional investors. There are greater positive effects of rational sentiments on stock returns than irrational sentiments. Conversely, there are significant (insignificant) negative effects of irrational (rational) sentiments on volatility. Also, we find asymmetric (symmetric) spillover effects of irrational (rational) bullish and bearish sentiments on the stock market. Evidence in favor of irrational sentiments is consistent with the view that investor error is a significant determinant of stock volatilities.     

Large returns,conditional correlation and portfolio diversification: a value-at-risk approach

This paper, using daily returns on 30 Dow Jones Industrial stocks for the period 1991-1999, investigates the possibility of portfolio diversification when there are negative large movements in the stock returns (i.e. when the market is bearish). We estimate the quantiles of stock return distributions using non-parametric and parametric methods that are widely being used in measuring value-at-risk (VaR). We find that the average conditional correlation of 30 stocks is much higher when the large movements are negative than that when the market is 'usual'. Further, we find that, contrary to the results of previous studies, there is no notable difference between the average conditional correlations when the large movements are positive and when the market is 'usual'. Moreover, it is evident from the results of the conditional CAPM that the portfolio's diversifiable and non-diversifiable risks, as measured by the error variance of the CAPM and beta respectively, are highly unstable when the market is bearish than that when it is 'usual' or bullish. The overall results suggest that the possibility of portfolio diversification would be eroded when the stock market is bearish. These findings have implications for portfolio diversification and risk management in particular and for finance in general. The ideas presented in this paper can be utilized for testing contagion in the international financial markets, a much-researched topic in international finance.     

Contrarian Profits and the Overreaction Hypothesis: the Case of the Athens Stock Exchange

This paper investigates the existence of contrarian profits and their sources for the Athens Stock Exchange (ASE). The empirical analysis decomposes contrarian profits to sources due to common factor reactions, overreaction to firm‐specific information, and profits not related to the previous two terms, as suggested by Jegadeesh and Titman (1995). Furthermore, in view of recent evidence that common stock returns are related to firm characteristics such as size and book‐to‐market equity, the paper decomposes contrarian profits to sources due to factors derived from the Fama and French (1993, 1996) three‐factor model. For the empirical testing, size‐sorted sub‐samples that are rebalanced annually are employed, and in addition, adjustments for thin and infrequent trading are made to the data. The results indicate that serial correlation is present in equity returns and that it leads to significant short‐run contrarian profits that persist even after we adjust for market frictions. Consistent with findings for the US market, contrarian profits decline as one moves from small stocks to large stocks, but only when market frictions are considered. Furthermore, the contribution to contrarian profits due to the overreaction to the firm‐specific component appears larger than the underreaction to the common factors.     

On the asymptotic normality of some unbiased estimates

Abstract

Let X _m(n) =(X _j , n, ..., X _{j m,n} ) be a subset of observations of a sample X_n = (X_1n X _2n ... , X _nn ). Here the X_jn 'S in Xn are not necessarily independent or identically distributed, and m(n) mayor may not tend to infinity as n tends to infinity. Suppose the joint density function h_n =h_n (x _m (n); θ) of the X _jn 's in Xm(n) is completely specified except the values of the parameters in the parameter vector θ = (θ1 θ2, ... , θ _k ), where θ belongs to a non-degenerate open subset H of the k-dimensional Euclidean space Rk and k?m(n).     

Asymmetric impacts of disaggregated oil price shocks on uncertainties and investor sentiment

This paper aims to examine short- and long-run asymmetries in the impacts of disaggregated oil price shocks on economic policy uncertainty, stock market uncertainty, treasury rates, and investor (bullish and bearish) sentiment in the US. To this end, we use a nonlinear auto-regressive distributed lag cointegration approach, which allows us to capture both positive and negative disaggregated oil shocks. We find that oil demand shocks are the main drivers of both measures of uncertainty, while oil supply shocks affect treasury rates. However, both oil demand shocks and oil supply shocks affect investor sentiment, with certain differences in the effects of positive and negative shocks. The overall effects of both oil demand and supply shocks—whether positive or negative—are stronger in the long-run than in the short-run. Additionally, we apply rolling causality and reveal evidence of a rather homogenous causal flow from disaggregated oil shocks to the variables studied, particularly around global stress periods. Our findings have implications for asset pricing and portfolio risk management and suggest policy formulations that differentiate between disaggregated positive and negative oil price shocks.

     

Bullish/bearish/neutral strategies under short sale restrictions

This study investigates the effects of short sale restrictions by extending the model of Dridi and Germain (2004) and infers informed traders’ strategies and the relation between order imbalance and price thereunder. The results are generally in line with the empirical evidence documented in the literature and are summarized as follows: First, seller-initiated trading incurs a greater price reaction. Second, short sale restrictions shift the skewness of asset returns. Third, the restrictions can stimulate investors to acquire information or increase each individual trader's order flow under the bullish and neutral signals as well as the bearish signal, which is yet to be explored empirically.