Worse Returns Research Articles

Financial Modelling System Using Deep Neural Networks (DNNs) for Financial Risk Assessments

ABSTRACTThe FOREX market assessment is a big challenge for investors and global risk managers. However, the present study uses daily multicurrency exchange rate returns data from 2007 to 2022 to estimate the learning returns performance of the proposed model to find a safe‐haven currency for optimal investment strategy. The categorical returns are classified into good returns (GRs), bad returns (BRs) and no returns (NRs). Therefore, the present study needs to use a one‐hot‐encoding function to convert a categorical dataset into a numeric format with TensorFlow. The present study proposes a deep neural network‐based multilayer perceptron (DNN‐based MLP) with a backpropagation algorithm to estimate the learning returns performance of the proposed model to find a safe‐haven currency for optimal investment strategy. The findings showed that currency exchange rate return 2 (CERR2) is relatively a safe‐haven currency than currency exchange rate return 1 (CERR1) and currency exchange rate return 3 (CERR3). Moreover, the findings also showed that the proposed model gives optimal learning return performance. This study may assist FOREX investors to modify their investment strategies under shed light of findings of the study. In addition, the findings of the present study may also support global risk managers to revisit their hedging strategies.

Is “Not Guilty” the Same as “Innocent”? Evidence from SEC Financial Fraud Investigations

When the Securities and Exchange Commission (SEC) investigates firms for financial fraud, investors learn about the investigation only if managers disclose it, or regulators sanction the firm. We investigate the effects of such disclosures using confidential records on all investigations, regardless of outcome. Even when no charges are brought, firms that voluntarily disclose an investigation underperform non‐disclosing firms by 11.7 percent over the following year. Disclosure is associated with a higher chance of shareholder class action lawsuits, and more prominent disclosures are associated with worse returns. CEOs who disclose an investigation are 14 percent more likely to experience turnover. Our results are consistent with transparency about bad news being punished by financial and labor markets.

Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets

This paper examines the asymmetric return spillovers between crude oil futures, gold futures and ten sector stock markets of China. The results show using the spillover index of Diebold and Yilmaz (2012, 2014) time-varying asymmetry spillovers among commodity and the ten sectors. Industrials and consumer discretionary sectors are the largest contributor and receiver of spillovers in the system. In addition, basic materials sector is a net contributor of spillovers whereas oil futures, gold futures and the remaining sectors are net receiver of spillovers. Furthermore, the bad return spillovers dominate the good return spillovers. The asymmetry spillovers are influenced by the global financial and European crises (GFC & ESDC), oil price crash and global health crisis (COVID-19 outbreak). Equity investors benefit from adding gold and oil to their individual equity markets. Moreover, the hedging is sensitive to the GFC & ESDC, oil price crash, and COVID-19 outbreak. Finally, the highest hedging effectiveness occurs during COVID-19 spread for the case of oil futures. The result is similar for gold under only good spillovers and it is highest during recovery period under bad spillovers.

Eastern Halloween effect: A stochastic dominance approach

The Eastern market wisdom of “May is poor, June is bleak, and July will turn around” unveils an international stock markets dynamic that lower returns in May followed by even worse return in June but rebounding back to an upward trend in July. This wisdom is termed as “Eastern Halloween” effect in this paper which has some similarities with the traditional Halloween Effect. However, the traditional risk approaches fail to explain this Eastern Halloween effect satisfactorily. We apply stochastic dominance method to examine the Eastern Halloween effect and provide solid evidence of existence of Eastern Halloween effect.

Measuring Sequence of Returns Risk

The authors discuss the nature and importance of the concept of sequence risk, the risk that a bad return occurs at a particularly unfortunate time, such as around the point of maximum accumulation or the start of decumulation. This concept is especially relevant in the context of retirement savings, where the implications for withdrawal rates of a bad return can be particularly severe. They show how the popular “glidepath” or target-date savings products are very exposed to such risk. Three different measures of sequence risk are proposed, each of which is intended to inform investors of the probability that a chosen investment strategy may not deliver desired withdrawal rates, and hence these measures are intended to aid investment choices; conventional performance measures such as Sharpe or Sortino ratios are only indirectly related to this ability to achieve a given withdrawal experience. Finally, the authors note that, using US data, very simple portfolios comprising equities and bonds can achieve very low probabilities of failure to achieve popular desired withdrawal rates, such as 5% a year, as long as the equity component is “smoothed” by switching in and out of cash using a simple trend-following rule.

Pembuktian Return Momentum dan Kontarian pada Saham Syariah

The purpose of this paper are to examine and analyse returns of momentum and contarian portofolio on Islamic stocks listed on the Jakarta Islamic Index 30 (JII 30) for the period 2010-2018. The method used in this study is Jagedeesh and Titmant (1993). Winner portfolio is formed by buying stocks with the best return performance in the past and selling stocks with bad returns in the past. Whereas a loser portfolio is formed by buying shares of poor return performance in the past and selling stocks with good returns in the past. Formations and observations used 1,3,6 and 12 months. With portfolio weighting based on equal-weighted and value-weighted. Return of momentum portofolio when winner minus loser positive. Return of contarian portofolio when loser minus winner positive. Significant contours are determined by a one-sample t-test using SPSS 25. The study did not find any return on the Islamic stocks listed on JII 30 for the period 2010-2018. But investors can still use this strategy to increase investment returns on Islamic stocks. Because this strategy still provides positive returns.

Developing a Measure of Sequence Risk

We discuss the nature and importance of the concept of Sequence Risk, the risk that a bad return occurs at a particularly unfortunate time, such as around the point of maximum accumulation or the start of decumulation. This is especially relevant in the context of retirement savings, where the implications for withdrawal rates of a bad return can be particularly severe. We show how the popular ‘glidepath’ or target date savings’ products are very exposed to such risk. Three different measures of Sequence Risk are proposed, each of which is intended to inform investors of the probability that a chosen investment strategy may not deliver desired withdrawal rates and hence these measures are intended to aid investment choices; conventional performance measures such as Sharpe or Sortino ratios are only indirectly related to this ability to achieve a given withdrawal experience. Finally, we note that, using US data, very simple portfolios comprising equities and bonds can achieve very low probabilities of failure to achieve popular desired withdrawal rates such as 5% p.a. providing the equity component is ‘smoothed’ by switching in and out of cash using a simple trend following rule.

Is 'Not Guilty' the Same as 'Innocent'? Evidence from SEC Financial Fraud Investigations

When the Securities and Exchange Commission (SEC) investigates firms for financial fraud, investors only learn about the investigation if managers disclose it, or regulators sanction the firm. We investigate the effects of such disclosures using confidential records on all investigations, regardless of outcome. Even when no charges are brought, firms that voluntarily disclose an investigation underperform non-disclosing firms by 11.7% over the following year. Disclosure is associated with a higher chance of shareholder class action lawsuits, and more prominent disclosures are associated with worse returns. CEOs who disclose an investigation are 14% more likely to experience turnover. Our results are consistent with transparency about bad news being punished by financial and labor markets.

Eastern Halloween Effect: A Stochastic Dominance Approach

The Eastern market wisdom of “May is poor, June is bleak, and July will turn around” unveils an international stock markets dynamic that lower returns in May followed by even worse return in June but rebounding back to an upward trend in July. This wisdom is termed as “Eastern Halloween” effect in this paper which has some similarities with the traditional Halloween Effect. However, the traditional risk approach both fail to explain this Eastern Halloween effect satisfactorily. We apply Stochastic Dominance method to examine the Eastern Halloween effect and provide a solid evidence of existence of Eastern Halloween effect.

Negative Impact of NPA on Indian Economy: An Analysis

Any asset which stops giving returns to its investors for a specified period of time is known as Non-Performing Asset (NPA). Indian Banking industry is seriously affected by Non-Performing Assets. More than Rs. 7 lakh crore worth loans are classified as Non-Performing Loans in India. This is a huge amount. The figure roughly translates to near 10% of all loans given. This means that about 10% of loans are never paid back, resulting in substantial loss of money to the banks. When restructured and unrecognised assets are added the total stress would be 15-20% of total loans. NPA crisis in India is set to worsen. Restructuring norms are being misused. This bad performance is not a good sign and can result in crashing of banks as happened in the sub-prime crisis of 2008 in the United States of America. Also, the NPA problem in India is worst when comparing other emerging BRICS Economies. In India banking sector has played pivotal role in our nation building. After Liberalisation of the economy the banking sector has faced severer challenges, but due to its solid foundation and management it has withered all the subsequent challenges including 2008 sub-prime crisis. But the recent problem has been grave. In the case of public sector banks, the bad health of banks means a bad return for a shareholder which means that government of India gets less money as a dividend. Therefore it may impact easy deployment of money for social and infrastructure development and results in social and political cost. NPAs related cases add more pressure to already pending cases with the judiciary one. In this paper it has been discussed in details with best possible solutions.

COMPARATIVE ANALYSIS OF THE FACE AND NECK SKIN ELASTICITY IN 20-30 YEARS WOMEN

Prevention and correction of external signs of the face and neck aging are the main direction of modern cosmetology. The study of the skin functions of various zones of face and neck will allow to carry out more effectively cosmetic effects. The aim is to study the features of the elasticity of facial skin (forehead, skin around the eyes, cheeks) and neck in patients without external signs of skin aging. Material and methods. Elasticity of the face and neck skin was determined in 63 women aged 20-30 years, who made up four comparison groups: in the 1st group (22 women) the elasticity of the forehead skin was studied, in the 2nd group (43 women) - around eye, in the third group (49 women) - cheeks, in the 4th group (24 women) - the neck. To measure the elasticity of the skin the Cutometer MPA 580 (“Courage Khazaka”, Germany) was used. Results. The comparative analysis was performed with the forehead skin. The skin around the eyes was 61-68% more stretchy (R0, R8), 65% worse returned to the initial state (R1), including 26-62% with repeated suction cycles sensor (R3, R4, R9), by 1-11% less elastic (R2, R5, R7), by elastic-viscous properties (R6) is 26% better, but overall its elasticity is lower by 35-53% (F0, F1). The cheek skin is 91- 104% more stretchy (R0, R8), worse returns to the initial state (R1) by 22%, including by 10-82% with repeated suction cycles by the sensor (R3, R4, R9),13% more elastic (R2) and 15% less elastic (R5, R7), the elastic-viscous properties (R6) 41% better, but overall its elasticity is lower by 31-100% (F0, F1) . The neck skin is 186-257% more stretchable (R0, R8), 6% worse returns to the initial state (R1), including 13-164% with repeated suction cycles by the sensor (R3, R4, R9), by 11-28% more elastic (R2, R5, R7), by elastic-viscous properties (R6) 50% better, but overall its elasticity is lower by 88-153% (F0, F1). Conclusion. The skin of the forehead, around the eyes, cheeks and neck, according to the elasticity indices, has significant differences. The obtained data should be taken into account when carrying out various cosmetology techniques, further study are needed.

Impact of persistent bad returns and volatility on retirement outcomes

We examine wealth outcomes and risk of ruin faced by retirees due to persistent bad returns and high volatility in equity markets occurring at different stages of their retirement. Our results show poor equity returns persisting over long periods can put retirement security to serious risk but volatile market conditions actually have the opposite impact. The timing of such persistent bad returns and volatility (early or late stages of retirement) is critical and has differing effects on retirement outcomes. The results are robust to varying portfolio allocations to equities although the precise impacts are different.

Bank Capital in the Crisis: Itts Not Just How Much You Have but Who Provides It

Bank capital is the cornerstone of bank regulation and is considered a key determinant of a bank’s ability to withstand economic shocks. In the area of bank capital regulation, the general view is that more bank capital is better, irrespective of who provides it. In this paper, we investigate whether the investment horizon of bank capital providers matters for bank performance during the recent financial crisis. We observe that banks with more short-term investor ownership have worse stock returns during the crisis. Further exploration suggests that this is partially because banks with higher short-term investor ownership took more risk prior to the crisis but mainly because they experienced higher selling pressure during the crisis. Our results confirm the economic benefit of bank capital in helping banks to perform better during crises. However, when we decompose bank capital by the nature of its providers, we show that more capital is associated with worse performance when it is provided by short-term institutional investors.

Problém "public-private pay gap" v České republice

This article analyzes the public-private pay gap in the Czech Republic. To prove the existence of this pay gap and measure for its decomposition, we used Mincer wage equations and Blinder-Oaxaca-based decomposition techniques. Using data from the IV/2012 Quarterly Survey of Average Earnings, an econometrical model intended for regression and decomposition analyses was set. The most important part of the model is the dummy variable representing pertinence to the public sector of the national economy. We found that this dummy was statistically significant throughout the wage distribution, and that the value of its coefficient was negative and rising. This means that public sector employees up from the lowest deciles can be discriminated against in their salaries, and that this potential for discrimination increases as we look at the higher deciles. This finding was further examined by decomposition analysis which showed that on average, public sector employees are better equipped (have better characteristics) but have worse return on them (lower regression coefficients).

Risk Interpretation of the CAPM's Beta: Evidence from a New Research Method

This study tests the validity of using the CAPM beta as a risk control in cross‐sectional accounting and finance research. We recognize that high‐risk stocks should experience either very good or very bad returns more frequently compared to low‐risk stocks, that is, high‐risk stocks should cluster in the tails of the cross‐sectional return distribution. Building on this intuition, we test the risk interpretation of the CAPM's beta by examining if high‐beta stocks are more likely than low‐beta stocks to experience either very high or very low returns. Our empirical results indicate that beta is a strong predictor of large positive and large negative returns, which confirms that beta is a valid empirical risk measure and that researchers should use beta as a risk control in empirical tests. Further, we show that because the relation between beta and returns is U‐shaped, that is, high betas predict both very high and very low returns, linear cross‐sectional regression models, for example, Fama–MacBeth regressions, will fail on average to reject the null hypothesis that beta does not capture risk. This result explains why previous studies find no significant cross‐sectional relation between beta and returns.

Measuring High-Frequency Causality between Returns, Realized Volatility and Implied Volatility

In this paper, we provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The leverage hypothesis asserts that return shocks lead to changes in conditional volatility, while the volatility feedback effect theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On observing that a central difference between these alternative explanations lies in the direction of causality, we consider vector autoregressive models of returns and realized volatility and we measure these effects along with the time lags involved through short-run and long-run causality measures proposed in Dufour and Taamouti (2010), as opposed to simple correlations. We analyze 5-minute observations on S&P 500 Index futures contracts, the associated realized volatilities (before and after filtering jumps through the bispectrum) and implied volatilities. Using only returns and realized volatility, we find a strong dynamic leverage effect over the first three days. The volatility feedback effect appears to be negligible at all horizons. By contrast, when implied volatility is considered, a volatility feedback becomes apparent, whereas the leverage effect is almost the same. These results can be explained by the fact that volatility feedback effect works through implied volatility which contains important information on future volatility, through its nonlinear relation with option prices which are themselves forward-looking. In addition, we study the dynamic impact of news on returns and volatility. First, to detect possible dynamic asymmetry, we separate good from bad return news and find a much stronger impact of bad return news (as opposed to good return news) on volatility. Second, we introduce a concept of news based on the difference between implied and realized volatilities (the variance risk premium) and we find that a positive variance risk premium (an anticipated increase in variance) has more impact on returns than a negative variance risk premium.

Community fundraising 2.0—the future of fundraising in a networked society?

Abstract Over recent years, individual donor fundraisers worldwide have seen the beginnings of a dramatic shift in the way that consumers react to mass fundraising communications, resulting in many of the ‘interruptive’ direct marketing approaches on which individual donor fundraising programmes are traditionally reliant delivering worse and worse returns. This paper examines how the growth of a networked society of increasingly sophisticated and independently informed consumers has contributed to this shift; illustrates how the rise of Web 2.0 has accelerated its impact and considers how individual donor fundraising may have to evolve to more effectively respond to this in the future. Copyright © 2009 John Wiley & Sons, Ltd.

Downside Risk analysis applied to the Hedge Funds universe

Hedge Funds are considered as one of the portfolio management sectors which shows a fastest growing for the past decade. An optimal Hedge Fund management requires an appropriate risk metrics. The classic CAPM theory and its Ratio Sharpe fail to capture some crucial aspects due to the strong non-Gaussian character of Hedge Funds statistics. A possible way out to this problem while keeping the CAPM simplicity is the so-called Downside Risk analysis. One important benefit lies in distinguishing between good and bad returns, that is: returns greater or lower than investor's goal. We revisit most popular Downside Risk indicators and provide new analytical results on them. We compute these measures by taking the Credit Suisse/Tremont Investable Hedge Fund Index Data and with the Gaussian case as a benchmark. In this way, an unusual transversal lecture of the existing Downside Risk measures is provided.

The Failure Mode and Effects Analysis Implementation for Laser Marking Process Improvement: A Case Study

Failure mode and effects analysis (FMEA) is a preventive technique in reliability management field. The successful implementation of FMEA technique can avoid or reduce the probability of system failure and achieve good product quality. The FMEA technique had applied in vest scopes which include aerospace, automatic, electronic, mechanic and service industry. The marking process is one of the back ends testing process that is the final process in semiconductor process. The marking process failure can cause bad final product quality and return although is not a primary process. So, how to improve the quality of marking process is one of important production job for semiconductor testing factory. This research firstly implements FMEA technique in laser marking process improvement on semiconductor testing factory and finds out which subsystem has priority failure risk. Secondly, a CCD position solution for priority failure risk subsystem is provided and evaluated. According analysis result, FMEA and CCD position implementation solution for laser marking process improvement can increase yield rate and reduce production cost. Implementation method of this research can provide semiconductor testing factory for reference in laser marking process improvement.

Weighing the Evidence: How Confident Are You with Confidence Intervals? (Hint: You Might Be Missing Something)

Statistical jargon isn’t particularly useful in everyday medical practice. When was the last time you mentioned P values, t tests, chi-square tests, or confidence intervals in a typical conversation with patients and families? However, I would argue that of the entities named in the previous sentence, the confidence interval (CI) is a concept worth a clinician’s time to understand.A confidence interval describes the range of plausible values for a result, given the information known from the study. Statistically, a 95% CI describes a range of values inside which the answer would lie 95 times, if the study were repeated 100 times. A more clinically useful description is the range within which we can be 95% sure the true population value lies. (Statistical purists cringe when they hear this definition, but it really isn’t that far off.)CIs and P values are mathematical cousins. If we accept statistical significance as a P value of <.05, a 95% CI that includes 0 (for studies looking at the absolute difference between 2 interventions) or 1 (for studies looking at ratios between 2 entities) in its range indicates that the study has not achieved this level of statistical significance.A P value says nothing about the magnitude of the effect being studied. A CI, in contrast, can be used to quantify the best case and worst case for the impact on a patient’s well-being one is likely to achieve. This is what makes it important for patient care.A range of plausible values — that is, a confidence interval — can be calculated for almost any study result, including those for diagnosis, treatment, and prognosis. The mathematical formula for CI involves the number of patients in the study, so that the more patients in a study, the narrower the CI. This makes sense: if a study has a large number of patients, chances are that individual variation and sampling error will affect the result, and the “true” result can be found within a smaller range.How would you use a CI? Let’s look at the article by Moro, et al, which showed that feeding a mixture of prebiotic oligosaccharides to newborns with a strong parental history of allergic disorders reduces the incidence of atopic dermatitis in the first 6 months after birth. They found a statistically significant difference between the rate of AD in the placebo group, 24%, and the rate in the group fed with prebiotics, which was 10%. The number needed to treat (NNT) in this instance is 1/(0.24–0.10), or 7. This NNT means that one would need to give 7 infants the prebiotic mixture to prevent 1 additional case of AD. That’s not a bad return on investment! However, one can calculate a 95% CI for this difference in proportions as ranging from 4 to 27. (For calculating CI for 2 proportions see http://vl.academicdirect.org/applied_statistics/binomial_distribution/ref/CIcalculator.xls.) The clinician may find it useful to consider whether the use of prebiotics is worthwhile if the “truth” is that 27 infants would need to receive this regimen to prevent one additional case of AD. A decision would depend on many other factors, including cost and difficulties in administering the treatment, adverse effects, and family preferences.So, don’t gloss over those CIs the next time you read an article. They give much more information than a P value and allow you to quantify the magnitude of the effect being measured. Below are other resources to learn about CIs.