Stock Price Ratios Research Articles

The Effect of the Movement in 52-Week High on Momentum Profit: The Evidence from Taiwan

Purpose: This paper aims to examine the impact of price movements in 52-week highs on a 52-week high momentum strategy. This study refers to the upward or downward movement in 52- week highs as an updating effect and determines how this effect influences the profitability of the original 52-week high momentum strategy. Design/methodology/approach: This paper decomposes the ratio of stock price to 52-week high into denoted two components: price change and updating components. We construct two momentum strategies, each focusing on adjusting either the price change or the updating component. Additionally, we employ a portfolio approach and Fama-MacBeth regression analysis to investigate the profitability of each proposed momentum strategy. Findings: The empirical results reveal that removing the price change component (updating component) from the original 52-week high measure can increases (decreases) the momentum profit, implying that the updating component dominates the price change component. Moreover, our analysis shows that when a high ratio of stock price to 52-week high is driven by a downward updating event, the subsequent positive momentum for a winner portfolio is more substantial. Research limitations/implications: This paper investigates the influence of 52-week highs movement on momentum strategies, utilizing data from Taiwan stock market. The findings reveal that accounting for the updating effect of 52-week highs can enhance the profitability of the original momentum strategy. However, it is important to note that this conclusion is currently limited to relatively inefficient stock markets. The impact on relatively efficient markets remains an area that requires further research for a comprehensive understanding. Originality/value: The finance literature widely acknowledges the 52-week high price as a reference point that can impact investors' trading psychology. Numerous empirical studies have confirmed the profitability of the 52-week high momentum investing strategy. However, these studies have not thoroughly explored the implications and effects of price movements within the scope of a 52-week high momentum strategy. Taking behavioral perspectives into account, this paper considers that the updating of 52-week high prices can influence investors' attention and subsequently impact the profitability of the momentum strategy.

PENGARUH RASIO LIKUIDITAS, PROFITABILITAS, SOLVABILITAS DAN HARGA SAHAM TERHADAP PENGUNGKAPAN ISLAMIC SOCIAL REPORT DI PERUSAHAAN SEKTOR KESEHATAN

ISR as a report that is not mandatory makes this disclosure interesting to be examined and juxtaposed with financial ratios, namely liquidity, profitability, solvency and stock price ratios. This study examines these financial ratios against simultaneous and partial ISR disclosures in health sector companies listed in ISSI for the 2017-2021 period. This study applied quantitative methods and multiple linear regression, the samples in this study were selected by purposive sampling techniques. The companies gathered are 12 companies. The results of the regression analysis carried out were simultaneously and partially variable liquidity, profitability, solvency, and stock price ratios, which did not significantly affect ISR disclosure.

Multi-scale local cues and hierarchical attention-based LSTM for stock price trend prediction

Stock price trend prediction is to seek profit maximum of stock investment by estimating future stock price tendency. Nevertheless, it is still a tough task due to noisy and non-stationary properties of stock market. Thus, it is important how to relieve such negative effects and to improve prediction accuracy. In this paper, we leverage four diverse local descriptors in short durations to alleviate noisy fluctuations of stock price. In detail, piecewise aggregate approximation (PAA) collects relatively stable average values; the derivatives of short-time series reflect the change ratio of stock price; the slope implies the short-time price trend; hog-1D aggregates different oriented gradients into histograms in a statistical fashion. They provide diverse and comprehensive cues about the stock price series across different aspects. Building upon such local descriptors, we propose a multi-scale local cues and hierarchical attention-based LSTM model (MLCA-LSTM) to capture the underlying price trend patterns. It has two advantages: 1) multi-scale information is further enriched by performing different scale sliding windows over stock price series to induce diverse local descriptors, 2) temporal dependency and multi-scale interactions are jointly attended and aggregated through the hierarchical attention mechanism and multi-branch LSTM structure. Experiments on the real stock price data confirm the efficacy of the proposed model as compared to the state-of-the-art counterparts.

The quantity theory of stock prices

ABSTRACT We determine the implications of the Modern Quantity Theory of Money for the nominal pricing of equity stocks. Our analysis is compatible with the hypothesis that the ratio of the quantity of money in circulation to a comprehensive index of stock prices evolves in terms of an elastic (that is, mean reverting) random walk. Using annual U.S. data covering the period from 1871 until 2018 shows that the ‘half-life’ of the period it takes for the money to stock price ratio to converge towards its long-run mean, is around fifteen years. Our empirical analysis also shows that unexpected inflation has a disruptive impact on the output and investment decisions implemented by firms and leads to an increase in the money to stock price ratio (equivalently, the stock price index falls relative to the quantity of money in circulation). We also develop a framework for determining how economic agents can rebalance their investment portfolios in response to disequilibria in the money to stock price ratio and thereby maximise the expected discounted utility obtained from their future consumption.

Investment Decisions through Total Asset Growth toward Price to Book Value on the Jakarta Islamic Index

This research analyzes the effect of investment decisions through Total Asset Growth (TAG) on Price to Book Value (PBV). Since PVB indicates stock measurement based on the ratio of stock price to book value, it is used by investors to assess the price offered. This research uses time-series data from 2014-2020 to examine seven companies selected using purposive sampling but based on fairly good asset developments from 2014 to 2020. Furthermore, this quantitative causal study data were collected using documentation from various sources and analyzed using a simple linear regression test. The results show that the TAG variable has no effect on PBV with a significance value of 0.89 0.05. This shows that TAG does not describe a stable company and is not always useful in investment decision-making.

Analisis Fundamental Sebagai Prediktor Harga Saham Sektor Telekomunikasi

The rapid development and growth of the economy requires companies to improve their business performance. Companies with good performance are generally reflected in their share prices. This makes the stock price one of the investors' preferences for investing. Deepening the development of stock prices can be through the company's fundamental analysis. The purpose of this study is to analyze the company's fundamental factors on stock prices. The research was conducted on the telecommunications sector on the IDX, with an observation period of 2017 - 2019. There is a total population and a sample of 5 issuers at the same time. This study uses secondary data and multiple linear regression analysis techniques. The findings of this study are from 5 (five) ratios used, current ratio, debt to equity ratio, and earnings per share individually have no significant effect on stock prices. Gross profit margin have a positive signifikan effect on stock price ratio of total asset turn over which has a negative significant effect on the stock price of telecommunications companies on the IDX.

Analysis of Equity Valuation Models and Target Price Accuracy: An Evidence From Analyst Reports in Indonesia

This study examines whether investment analysts (sell-side) in Indonesia tend to prefer cash-flow-based valuation models over the accrual-based valuation model, how the accuracy of valuation models are used, and whether the use of both valuation models simultaneously for generating target prices can improve accuracy. The researchers conducted a comprehensive content analysis of 99 equity research reports for most companies listed in the LQ-45 index. The results show that in the accrual-based valuation model, in particular, the ratio of stock price to income (P/E) was the most popular valuation model that appeared in equity reports in all sectors. However, from the perspective of the valuation model as the producer of the target price (dominant valuation), discounted cash flow (DCF) was the most popular valuation model used. It was also found that the cash-flow-based valuation model gave the highest accuracy. In addition, the researchers also found significant results in the Chi-square test which showed the use of both valuation models simultaneously could improve the valuation results more precisely by the analysts. This was in line with the intuition that the accrual concept adds value to the relevance of the information to cash flow.

The Accuracy of Various Value Drivers of Price Multiple Method in Determining Equity Price

Stock price multiple is one of the most well-known equity valuation technique used to forecast equity price. It measures by multiplying “the ratio of stock price to a value driver” by a value driver. The value driver can be earning per share (EPS), sales or other financial measurements. The objective of price multiple technique is to evaluate the value of assets and compare how similar assets are priced in the market. Although stock price multiple technique is common in financial filed, studies on the application of the technique in Thailand is still limited. The present study is conducted to serve three major objectives. The first objective is to apply the technique to measure value of firms in banking sector in the Stock Exchange of Thailand. The second objective is to develop composite price multiple index to forecast equity prices. The third objective is to compare valuation accuracy of different value drivers of price multiple (i.e. EPS, Earnings Growth, Earnings Before Interest Taxes Depreciation and Amortization, Sales, Book Value and Composite Index) in forecasting equity prices. Results indicated that EPS is the most accurate value drivers of price multiple used to forecast equity price of firms in baking sector.

Predicting firm level stock returns: Implications for asset pricing and economic links

We examine the predictive ability of stock price ratios, stock return dispersion and distribution measures for firm level returns. Analysis typically focusses on market level returns, however, for the underlying asset pricing model to hold, firm-level predictability should be present. Additionally, we examine the economic content of predictability by considering whether the predictive coefficient has the theoretically correct sign and whether it is related to future output growth. While stock returns reflect investor expectations regarding future economic conditions, they are often too noisy to act as predictor. We use the time-varying predictive coefficient as it reflects investor confidence in the predictive relation. Results suggest that a subset of stock price ratios have predictive power for individual firm stock returns, exhibit the correct coefficient sign and has predictive power for output growth. Each of these ratios has a measure of fundamentals divided by the stock price and has a positive relation with stock returns and output growth. This implies that as investors expect future economic conditions to improve and earnings and dividends to rise, so expected stock returns will increase. This supports the cash flow channel as the avenue through which stock return predictability arises.

THE VALUATION OF EXECUTIVE STOCK OPTIONS UNDER GARCH MODELS

In this paper, we investigate executive stock options with endogenous departure and time-varying variances. We use a “Generalized Autoregressive Conditional Heteroskedasticity” process to capture the variance process of the log stock price. In addition, we take into consideration the departure risk of the executive and assume that the probability of remaining employed has a power form of stock price ratios. After deriving the closed-form pricing formulae of executive stock options, we illustrate the effects of the departure risk on the values of executive stock options.

Comparison of the fundamental and monetary models of the Canadian dollar/US dollar exchange rate

This paper finds that a higher real interest rate differential, a higher output ratio and a higher differential of the government deficit as a percent of GDP cause the Canadian dollar to depreciate whereas a higher stock price ratio, a higher productivity ratio, and a higher commodity price cause the Canadian dollar to appreciate. In comparison, the fundamental model performs better than the monetary models in both the in-sample and out-of-sample forecasts.

Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices

We investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for predicting stable stock price ratios, defined in a cointegration model. Using this class of models and the proposed inferential technique, we are able to connect estimation and model uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior, which is shown to be equivalent to a Jeffreys’ prior, has under an orthogonal normalization for the selection of pairs of cointegrated stock prices and further, its effect for the estimation and prediction of the spread between cointegrated stock prices. We distinguish between models with a normal and Student t distribution since the latter typically provides a better description of daily changes of prices on financial markets. As an empirical application, stocks are used that are ingredients of the Dow Jones Composite Average index. The results show that normalization has little effect on the selection of pairs of cointegrated stocks on the basis of Bayes factors. However, the results stress the importance of the orthogonal normalization for the estimation and prediction of the spread—the deviation from the equilibrium relationship—which leads to better results in terms of profit per capital engagement and risk than using a standard linear normalization.

Comovement of Stock Markets—An Analysis by Nonlinear Cointegration*

This paper proposes and estimates a statistical model of nonlinear cointegration, with applications to the stock markets of Japan and the United States. We define nonlinear cointegration as a long-run stable relationship between two time series variables even in the presence of temporary nonlinear divergence from this long-run relationship. More concretely, extending the bubble model of Asako and Liu (2013) [1] to stock price ratio variables, both upward and downward divergent bubble processes are estimated at a time. We conclude that, although two stock price indexes are not linearly cointegrated, they are considered to be cointegrated nonlinearly.

Optimal hedging in an extended binomial market under transaction costs

We develop an approach to optimal hedging of a contingent claim under proportional transaction costs in a discrete time financial market model which extends the binomial market model with transaction costs. Our model relaxes the binomial assumption on the stock price ratios to the case where the stock price ratio distribution has bounded support. Non-self-financing hedging strategies are studied to construct an optimal hedge for an investor who takes a short position in a European contingent claim settled by delivery. We develop the theoretical basis for our optimal hedging approach, extending results obtained in our previous work. Specifically, we derive a no-arbitrage option price interval and establish properties of the non-self-financing strategies and their residuals. Based on the theoretical foundation, we develop a computational algorithm for optimizing an investor relevant criterion over the set of admissible non-self-financing hedging strategies. We demonstrate the applicability of our approach using both simulated data and real market data.

Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices

We investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for predicting stable stock price ratios, defined in a cointegration model. Using this class of models and the proposed inferential technique, we are able to connect estimation and model uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior, which is shown to be equivalent to a Jeffreys’ prior, has under an orthogonal normalization for the selection of pairs of cointegrated stock prices and further, its effect for the estimation and prediction of the spread between cointegrated stock prices. We distinguish between models with a normal and Student t distribution since the latter typically provides a better description of daily changes of prices on financial markets. As an empirical application, stocks are used that are ingredients of the Dow Jones Composite Average index. The results show that normalization has little effect on the selection of pairs of cointegrated stocks on the basis of Bayes factors. However, the results stress the importance of the orthogonal normalization for the estimation and prediction of the spread — the deviation from the equilibrium relationship — which leads to better results in terms of profit per capital engagement and risk than using a standard linear normalization.

Pengaruh Return on Assets dan Debt to Equity Ratio terhadap Harga Saham pada Institusi Finansial di Bursa Efek Indonesia

This study aims to determine the effect of Return on Assets and Debt to Equity Ratio of Stock Price on Financial Institutions in Indonesia Stock Exchange. This study used secondary data, with samples 2 financial companies in Indonesia Stock Exchange during the study period 2004-2010. Independent variables in this study are Return on Assets and Debt to Equity Ratio. This study used purposive sampling technique. The method of data analysis used classical assumption test, hypothesis test, multiple regression analysis, the F test and t test. Based on results of the study, Return on Assets and Debt to Equity Ratio have no significant effect on stock price. Meanwhile, the F test result shows that Return on Assets and Debt to Equity Ratio jointly have no effect on stock price.

A dynamic analysis of stock price ratios

Stock price ratios have long been used by finance practitioners as a relative value metric. A popular argument for this widespread use is that stock price ratios tend to revert to their long-run mean so that substantial deviations from historical averages could successfully be arbitraged away. In this work, we lay out the theoretical conditions for the ratio of stock prices to be a stationary process. In particular, we theoretically relate price ratio stationarity to economic mean reversion in profitability (as measured by dividends or earnings price ratios) across securities. We further test our theoretical predictions using a popular example of ‘close’ stocks.

A Dynamic Analysis of Stock Price Ratios

Stock price ratios have long been used by finance practitioners as a relative value metric. A popular argument for this widespread use is that stock price ratios would tend to revert to their long-run mean so that substantial deviations from historical averages could successfully be arbitraged away. In this work, we lay out the theoretical conditions for the ratio of stock prices to be a trend stationary process. In particular, we establish that, in the context of our model, market completeness entails stationary price ratios. We also theoretically relate statistical price ratio stationarity to economic mean reversion in profitability (as measured by dividends or earnings price ratios) across securities. We further test our theoretical predictions using standard unit root tests and cointegration analysis on a popular example of stocks. To illustrate the implications of the theoretical work, we provide a simple empirical exercise where we analyze the time series behavior of the Coca Cola and Pepsi stock price ratio. These two stocks provide us with a straightforward example of relative pricing between close substitutes. Our results have important implications for practitioners who seek to apply pairs-trading investment strategies in the stock market as they gives clear economic intuition to this popular practice. Indeed, as long as theoretical requirements are met, an investment strategy that exploits short-term error deviations of stock prices of close firms apart from their long run (cointegrated) relation, e.g., matching stocks by minimizing the sum of squared deviations between normalized stock prices as in Gatev, Goetzmann, and Rouwenhorst (2006) should produce significant risk-adjusted returns.

A rolling MTAR model to test for efficient stock pricing and asymmetric adjustment

The paper is concerned with the question of whether the pricing of US stocks has been efficient in terms of the present value model. The MTAR model used in the context of market efficiency is extended by means of a rolling window estimation strategy. This rolling MTAR analysis revealed that the assumed underlying time series process in the logarithm of earnings to stock-price ratio is highly unstable over time. The findings cast doubt on the usefulness of conclusions relating to extended time periods of about 130 years. Because of the rolling MTAR approach, it is possible to reveal decades of inefficient stock pricing as well as decades of assymetrical time series behaviour.

Stock market trading strategies based on earnings and cash flows in Finland: alternative risk-adjusting approach

This paper investigates whether the predictability of risk-adjusted stock returns using the ratio of earnings to stock price and the ratio of cash flow earnings to stock price disappears, when accounting-based risk measures are used for risk-adjusting purposes. The main contribution of the paper is to extend the approach suggested by Chan and Chen (1991) to other trading strategies and accounting-based risk measures. Consistent with the hypothesis developed, the empirical results indicate that earnings-to-price ratios lose their ability to predict stock returns when a firm’s financial and business risks are used to measure the risk of its stock. The results also indicate that these accounting-based risk measures can weaken but not totally negate the ability of the cash-flow earnings to stock price ratio to predict risk-adjusted stock returns.