If you trade the forex markets regularly, chances are that a lot of your trading is of the short-term variety; i. From my experience, there is one major flaw with this type of trading: h igh-speed computers and algorithms will spot these patterns faster than you ever will. When I initially started trading, my strategy was similar to that of many short-term traders. That is, analyze the technicals to decide on a long or short position or even no position in the absence of a clear trendand then wait for the all-important breakout, i. I can't tell you how many times I would open a position after a breakout, only for the price to move back in the opposite direction - with my stop loss closing me out of the trade. More often than not, the traders who make the money are those who are adept at anticipating such a breakout before it happens.

One of the principal is to check the worth or intrinsic value of the stock before investing. Intrinsic value of stock is the actual value of stock determined by the fundamentals of company irrespective of the market value.

It is an important aspect because it determines whether the stock is undervalued or overvalued and let the investor take advantage of temporary mispricing of stocks. It is always advantageous to invest in undervalued stock, because if stock price of undervalued company falls , it is still worth as someday the price is likely to recover but if stock price of overvalued company crashes it is unlikely to regains its former inflated value and history has proved it.

This is what happened in technology bubble bust. Another principal of value investing is margin of safety. It means buying stock in less than its intrinsic value or in simpler terms buying stock at discount. Margin of safety gives edge over just blindly buying a stock along with assurance that the company can survive during poor economic times. If the stocks are dear and valuation are exceeding intrinsic value and there is no margin of safety you sell.

Forget the noise that swirling around you, use your own knowledge and sense to pick up the stock. This implies that longer one invest in equity probability of negative return decreases. Simple logic for investment is to see intrinsic value and margin of safety of stock and invest for long term and your investment will be sort.

Open an Account. Any investor expectations that fundamental values did not materialise have now been discounted by much more than can be expected. We believe that value investors are now facing one of the best opportunities to earn substantial returns since the peak of the tech bubble.

Playing the convergence of stock prices towards fundamental values remains a sensible investment philosophy: convergence is the likely trend in coming years. In our next article, we investigate the expected consequences of a compression in value spreads on the performance of various equity factor styles and their multi-factor combinations.

Value stocks — Different definitions can mean significantly different outcomes. Any views expressed here are those of the author as of the date of publication, are based on available information, and are subject to change without notice. Individual portfolio management teams may hold different views and may take different investment decisions for different clients.

This document does not constitute investment advice. The value of investments and the income they generate may go down as well as up and it is possible that investors will not recover their initial outlay. Past performance is no guarantee for future returns. Investing in emerging markets, or specialised or restricted sectors is likely to be subject to a higher-than-average volatility due to a high degree of concentration, greater uncertainty because less information is available, there is less liquidity or due to greater sensitivity to changes in market conditions social, political and economic conditions.

Some emerging markets offer less security than the majority of international developed markets. For this reason, services for portfolio transactions, liquidation and conservation on behalf of funds invested in emerging markets may carry greater risk. A round-up of this week's key economic and market trends, and insights on what to expect going forward. Value spreads are back at tech bubble levels across markets… A period marked by a notably large expansion of value spreads and underperformance of value stocks was the tech bubble of the late s.

LinkedIn Twitter Facebook Email. Will value stocks continue to outperform expensive sector peers? Agne Rackauskaite. Daniel Morris. Talking heads — Solutions to improve food security. Asset allocation update — A deeper short in European equities. Disruptive technology — Maintaining course in volatile markets.

Talking heads — Shortlisting listed real estate. Weekly insights, straight to your inbox A round-up of this week's key economic and market trends, and insights on what to expect going forward. I have read and agree to the general terms and conditions of the website and I accept to receive the Investors' Corner newsletter. Article added to your bookmarks.

In investing, R-squared is generally interpreted as the percentage of a fund or security's movements that can be explained by movements in a benchmark index. For example, an R-squared for a fixed-income security versus a bond index identifies the security's proportion of price movement that is predictable based on a price movement of the index.

It may also be known as the coefficient of determination. A higher R-squared value will indicate a more useful beta figure. R-Squared only works as intended in a simple linear regression model with one explanatory variable. With a multiple regression made up of several independent variables, the R-Squared must be adjusted.

The adjusted R-squared compares the descriptive power of regression models that include diverse numbers of predictors. Every predictor added to a model increases R-squared and never decreases it. Thus, a model with more terms may seem to have a better fit just for the fact that it has more terms, while the adjusted R-squared compensates for the addition of variables and only increases if the new term enhances the model above what would be obtained by probability and decreases when a predictor enhances the model less than what is predicted by chance.

In an overfitting condition, an incorrectly high value of R-squared is obtained, even when the model actually has a decreased ability to predict. This is not the case with the adjusted R-squared. Beta and R-squared are two related, but different, measures of correlation but the beta is a measure of relative riskiness. A mutual fund with a high R-squared correlates highly with a benchmark. If the beta is also high, it may produce higher returns than the benchmark, particularly in bull markets.

R-squared measures how closely each change in the price of an asset is correlated to a benchmark. Beta measures how large those price changes are relative to a benchmark. Used together, R-squared and beta give investors a thorough picture of the performance of asset managers. A beta of exactly 1. Essentially, R-squared is a statistical analysis technique for the practical use and trustworthiness of betas of securities.

R-squared will give you an estimate of the relationship between movements of a dependent variable based on an independent variable's movements. It doesn't tell you whether your chosen model is good or bad, nor will it tell you whether the data and predictions are biased.

A high or low R-square isn't necessarily good or bad, as it doesn't convey the reliability of the model, nor whether you've chosen the right regression. You can get a low R-squared for a good model, or a high R-square for a poorly fitted model, and vice versa. In some fields, such as the social sciences, even a relatively low R-Squared such as 0. In other fields, the standards for a good R-Squared reading can be much higher, such as 0.

In finance, an R-Squared above 0. This is not a hard rule, however, and will depend on the specific analysis. Essentially, an R-Squared value of 0. For instance, if a mutual fund has an R-Squared value of 0. Here again, it depends on the context. Suppose you are searching for an index fund that will track a specific index as closely as possible. Advanced Technical Analysis Concepts.

Financial Ratios. Risk Management. Financial Analysis. Quantitative Analysis. Your Money. In terms of hedge funds, the R2 is used to determine the risk that is associated with some of the factors — including the risk models of the funds. For mutual fund performance, R2 is used mainly by investors to determine the correlation between the movement of a certain fund and a benchmark index — in this case, R2 is a historical measure. While the explanation given for R-Square seems a bit complex, the formula to calculate this coefficient is quite simple.

First, you have to divide the First Sum of Errors by the Second Sum of Errors, after which you subtract the final result from 1. However, this equation is only the last step when calculating the R2 of a set data point. After the line of best fit is in place, the analysts can safely come up with an error squared equation, thus keeping any errors within a relevant range.

In the end, when you have completed the list of errors, you add them up and then apply the R-Squared formula on them. Basically, R-Squared is a type of statistic that can offer some information about the goodness of fit of a given model. The coefficient of determination is a statistical measure and it shows how well and if the regression predictions are close to the real data points. In order to be sound in your financial goals, you need to have a great source of knowledge on every Read more.

This article covers the far-reaching topic of the asset coverage ratio. The retention ratio or plow back ratio is the calculation that shows you exactly what percentage of your net income The Formula of R-Squared While the explanation given for R-Square seems a bit complex, the formula to calculate this coefficient is quite simple. Conclusion Basically, R-Squared is a type of statistic that can offer some information about the goodness of fit of a given model.