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Business angels in Germany
(2000)
This paper looks at the value-relevance of accounting data and measures of web-traffic for Internet firms listed on the Neuer Markt. In particular, the objective is to identify value drivers during the period from October 1999 to May 2000. In doing so, the study attempts to contribute to the understanding of the investment behaviour of market participants during that time, in a market environment characterised by rapid technological change and growth. The study subdivides Internet companies into Ecommerce and Enabler firms and analyses the value-relevance accordingly. It emerged that, across both samples, no significant value-relevance of traditionally applied financial valuation metrics such as earnings and cashflow could be evidenced. However, a positive association of total sales with market capitalisation can be shown for both samples, and in addition, sales and marketing expenses (Ecommerce) as well as research and development costs (Enabler) can also be identified as value drivers. Furthermore, the paper finds a number of webmetrics to be highly value-relevant and positively associated with market capitalisation, viz. customer loyalty, reach, page impressions and unique visitors. Combining and comparing the information content and value-relevance supports the notion that webmetrics, which are not part of standardised reporting regulations, did provide at least as much explanatory power for variations in market value as standardised accounting data.
In search of alpha
(2015)
In this study we develop a trading strategy that exploits limited investor attention. Trading signals for US S&P 500 stocks stocks are derived from Google Search Volume data, taking a long position if investor attention for the corresponding security was abnormally low in the past week. Our strategy generates 19% average annual return and thereby outperforms a simple market buy-and-hold strategy. After controlling for the well-known risk factors, a significant alpha (abnormal return) of 10% p.a. remains. Returns are sufficiently large to cover transaction costs.
This study provides novel insights to the ongoing debate how market efficiency is challenged by investor behavior. Applying search engine data we find that retail investor attention can enhance market efficiency. High attention is associated with better incorporation of idiosyncratic stock information, which we interpret as improved pricing efficiency. This effect is even more pronounced in bullish markets. In bearish markets, however, retail investor attention leads to a deterioration of pricing efficiency, which might be explained with herding behavior. Our evidence holds for a broad sample of European and US stocks.
The constant introduction of new products is of great importance for the long-term financial success of companies. Newly launched products in consumer goods and services markets show high failure rates, often reaching 50%. In order to reduce flop rates, companies can integrate innovative and knowledgeable customers, so called 'lead users', into the new product development process. However, the detection of such lead users is difficult, especially in consumer goods markets with very large customer bases. A new and potentially valuable approach for the identification of lead users are virtual stock markets, which have been proposed and applied for political and business forecasting, but not for expert identification yet. The goal of this paper is to analyze theoretically and empirically the feasibility of virtual stock markets for lead user identification. We find in our empirical study that virtual stock markets are an effective instrument to identify lead users in consumer goods markets. Using the proposed method, companies operating in these markets can identify lead users more easily and integrate them into new product development projects. Thus, they can improve the innovation processes and reduce new product flop rates.