Financial Factors Affecting Price-to-Earnings Ratios in Canada

Purpose: The purpose of this study is to ascertain which financial factors affect the price-to-earnings ratios of Canadian firms. Methodology: A sample of 578 Canadian firms, across 11 industries, listed on the Toronto Stock Exchange during 2011 to 2018 is examined. Stock prices and financial statements accounts data is collected from S & P Capital IQ. We compute 27 financial factors to use as independent variables to regress on the price-to-earnings ratio dependent variables employing the Statistical Package for Social Sciences (SPSS) utilizing the software program’s forced, forward, and backward selection methods. Robustness tests are conducted using alternative dates (after the fiscal year end) to discover which model of financial factors best explains the forward price-to-earnings ratio as well as other statistical methods such as analysis of variance. Results: We find a unique model for each of the 3 models based on the forward price-to-earnings ratio date. The financial factors that explain each of the dates after the end of the fiscal year (1 month, 2 months, and 3 months) are the 4 variables: net profit margin, return on investment, total asset turnover, and the natural logarithm of the total assets. For model 3 (1 month after fiscal year end), in addition to the previous 4 factors, the dividends per share is part of the regression equation. All 3 models have strong statistically significant results at an alpha level of one percent. Further, industry effects are deduced and presented. Unique contribution to theory, policy, and practice: The results are unique to a Canadian sample of firms post- International Financial Reporting Standards (IFRS) adoption. Companies can utilize the empirical findings to manage their financial performance to maximize their price-to-earnings ratio. A product of a firm’s higher price-to-earnings ratio is a lower cost of capital which expands the corporation’s investment opportunities. Investors can apply this research to develop investment strategies hinged on price-to-earnings ratios to augment investment returns.

Sensitivity analysis in the infinite dimensional Heston model

We consider the infinite dimensional Heston stochastic volatility model proposed in Ref. 7. The price of a forward contract on a non-storable commodity is modeled by a generalized Ornstein–Uhlenbeck process in the Filipović space with this volatility. We prove a representation formula for the forward price. Then we consider prices of options written on these forward contracts and we study sensitivity analysis with computation of the Greeks with respect to different parameters in the model. Since these parameters are infinite dimensional, we need to reinterpret the meaning of the Greeks. For this we use infinite dimensional Malliavin calculus and a randomization technique.

Application of Option Pricing Framework for Analyzing and Predicting Stock Prices in Energy Markets

In this study, the evaluation of the pricing framework for predicting West Texas Intermediate crude oil stock was implemented where detailed analysis with varying changepoint shows that an arbitrage-free forward price can be derived from the buy-and hold strategy in the energy market thereby enabling investors in the market willing to be salvage from the market uncertainties as well as Arrow-Debreu situations to execute a spot or forward contracts depending on the time and place the market becomes favorable.

Financial market buoyancy is fragile amid rising risks

Subject Financial market momentum. Significance Global bond and equity markets continue to rally after making their largest annual gains since 2010 last year. Markets are brushing off a plethora of risks, from the escalation in tensions between Tehran and Washington to concerns about weak global growth, particularly in Europe. While valuations are becoming dangerously stretched -- the forward price-to-earnings ratio of the benchmark S&P 500 index is at the highest since 2011 -- the absence of a credible catalyst for a sharp sell-off is helping to underpin positive sentiment. Financial conditions remain exceptionally loose. Impacts Demand for government bonds is building momentum and the ten-year US treasury yield is just 40 basis points above its all-time low of 2017. Emerging-market bond and equity fund inflows have momentum and while several risks could curb this, a sustained reversal is unlikely. The Shanghai stock market has lost 5% since Wuhan’s coronavirus outbreak spread beyond China on January 13; further falls are likely. Despite low market volatility, government debt markets are much more pessimistic than equity markets about global growth prospects.

Adjustment to Risk Free Rate/ Violation of Put-Call Parity

The present value of a forward contract for any asset that does not pay a dividend is calculated by discounting its forward price by the risk-free rate. We show that the discount function for assets that have a non-zero correlation with interest rates, has to be adjusted to account for the correlation between the asset and interest rates. Put-Call parity is also violated and needs to be adjusted as well for such assets. It is shown that the risk-free rate is asset dependent. The adjustment to the price is small for short dated forwards, but increases quadratically with time to maturity.

Volatility and Market Integration of Spot-Forward Corn Price in Indonesia

This study aims to determine the volatility and market integration between the price of corn in the Indonesian spot market and futures market in the international market. The data used in this research is secondary data consisting of Indonesian corn spot price and corn forward price referring commodity exchange, Chicago. Data in the form of monthly time series in 2007 until 2016. ARCH / GARCH method is used to measure the volatility at spot and forward price, whereas the market integration of spot and forward corn is used Johansen Cointegration and Engel-Granger Causality method. The results show that spot and forward prices of corn occur high volatility. The best ARCH/GARCH model for spot price is GARCH (2,0) with the volatility value of 0,91 and for forward price is GARCH (2,0) with the volatility value of 1.12. It means that volatility of spot and forward influenced by the increase and fluctuations of spot and forward price two previous periods. Between the spot and forward market, there is market integration and a one-way causal relationship. The market integration indicates there is long-run relationship, while one way indicates the spot price effect on the forward price, not vice versa.

A Resolution to Valuation Conflicts of Swaptions/Caps and OIS/LIBOR

In this article, the authors provide a unified valuation framework under which a multicurve economy can be established and caps/floors and swaptions can be consistently priced. Furthermore, if a lognormal distribution is employed for the forward price (or 1 plus forward rate), then a “model-free” volatility calibration can be achieved, and all swaptions and caps/floors are perfectly repriced. This article leverages earlier work by Chen, Hsieh, and Huang (2017) who fix a crucial drift-adjustment problem of the traditional LIBOR market model (LMM) where the LIBOR rates follow a lognormal distribution. By assuming 1 + LIBOR to be lognormal (hence LIBOR is shifted lognormal), Chen, Hsieh, and Huang achieve an exact and deterministic drift-adjustment term. In this article, they extend the model to provide a perfect calibration to both swaptions and caps/floors (which is not doable under the traditional LMM), and by using a foreign currency analogy, they show that the model supports multiple curves, which is a key element to overnight index swap (OIS) discounting.