Correlation Between Ford and Westone Information
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By analyzing existing cross correlation between Ford Motor and Westone Information Industry, you can compare the effects of market volatilities on Ford and Westone Information and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Ford with a short position of Westone Information. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and Westone Information.
Diversification Opportunities for Ford and Westone Information
0.33 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Ford and Westone is 0.33. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and Westone Information Industry in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Westone Information and Ford is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Ford Motor are associated (or correlated) with Westone Information. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Westone Information has no effect on the direction of Ford i.e., Ford and Westone Information go up and down completely randomly.
Pair Corralation between Ford and Westone Information
Taking into account the 90-day investment horizon Ford is expected to generate 14.53 times less return on investment than Westone Information. But when comparing it to its historical volatility, Ford Motor is 1.92 times less risky than Westone Information. It trades about 0.02 of its potential returns per unit of risk. Westone Information Industry is currently generating about 0.18 of returns per unit of risk over similar time horizon. If you would invest 1,238 in Westone Information Industry on September 5, 2024 and sell it today you would earn a total of 538.00 from holding Westone Information Industry or generate 43.46% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 90.63% |
Values | Daily Returns |
Ford Motor vs. Westone Information Industry
Performance |
Timeline |
Ford Motor |
Westone Information |
Ford and Westone Information Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ford and Westone Information
The main advantage of trading using opposite Ford and Westone Information positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford position performs unexpectedly, Westone Information can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Westone Information will offset losses from the drop in Westone Information's long position.The idea behind Ford Motor and Westone Information Industry pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.Westone Information vs. Hangzhou Weiguang Electronic | Westone Information vs. Epoxy Base Electronic | Westone Information vs. Shanghai Broadband Technology | Westone Information vs. Leyard Optoelectronic |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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