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Customer sentiment eliminates bad trades

by Helen Dunne on 30/08/2011 17:25:21 in CorpComms Online | share me: del.icio.us | digg | reddit | Tweet

New service that measures customer sentiment predicts stock market and sales trends

About the author:

Helen Dunne

Helen Dunne is the editor of CorpComms Magazine, follow her tweets here @CorpCommsMag

Customer sentiment eliminates bad trades

A new data service for fund managers and investors that offers insight into customer sentiment expressed on social media sites, blogs and online message boards has been launched.

Social market analytics firm WiseWindow will offer up-to-date customer sentiment for American-headquartered consumer facing companies. It will initially focus on the airline industry.

The service has been tested by consulting firm Emerald Logic, which found that factoring in WiseWindow data into trading decisions boosted returns by more than 30 per cent for General Motors, Ford and Southwest Airlines on an annualised basis.

Emerald Logic chief executive Patrick Lilley said that his company had taken a simple momentum trading model, which is capable of beating the market, but filtered its decisions using the WiseWindow data. 

If the trading model suggested buying a particular stock but WiseWindow's data indicated customer sentiment was poor, Lilley did not make the purchase. 'I found that it eliminated more bad trades than it eliminated good trades,' he told BrainYard News.

WiseWindow chief executive Sid Mohasseb conceded that investment decisions should not solely be made on its data, but suggested investors added the insight to their own trading models. 'The farther you get from the consumer, the more questionable the data becomes,' he added. 

But Mohasseb claimed that, within the consumer sector, customer sentiment analysis is effective in predicting both stock market and sales trends. The correlation for share prices is 'upwards of 0.88' two to three weeks in advance, he claimed, while the correlation for sales trends is between 0.7 and 0.8 for a two week forecast.

 

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