Objective research to aid investing decisions
Menu
Value Allocations for July 2019 (Final)
Cash TLT LQD SPY
Momentum Allocations for July 2019 (Final)
1st ETF 2nd ETF 3rd ETF

Watsonizing Financial Markets?

Posted in Sentiment Indicators

Is information technology moving in on qualitative event trading just as it has high-frequency quantitative algorithm trading? In the October 2011 version of their paper entitled “Event Driven Trading and the ‘New News'”, David Leinweber and Jacob Sisk examine the trading acumen of a model (set of filters) trained to exploit Thomson Reuters News Analytics metadata (sentiment tone, stock relevance and novelty as measured by link counts). Their portfolio simulation approach: (1) is restricted to the technology, industrials, health care, financials and basic materials sectors; (2) assumes an extreme sentiment day for a stock has at least four novel news items (prior to 3:30PM in New York) and is among the top 5% of average daily positive or negative events; (3) makes portfolio changes at market close; (4) holds positions for 20 days, subject to a 5% stop-loss rule and a 20% take-profit rule; (5) constrains any one position to 15% of portfolio value; and, (6) assumes round-trip trading friction of 0.25%. Using news metadata for the S&P 1500 and associated stock returns during 2003 through 2009 for in-sample testing and the first three quarters of 2010 for out-of-sample testing, they find that:

  • In-sample testing indicates that:
    • There is a trade-off in filter settings between number of signals generated and signal exploitability.
    • Negative sentiment signals are more exploitable than positive signals.
    • Signals for small and medium capitalization stocks are stronger than those for large capitalization stocks.
    • Returns are volatile, with maximum drawdown about 60%. Mean monthly return is 1.7%, with 52% of months profitable.
    • The filter models starts producing alpha in 2007 when the Thomson Reuters News Analytics metadata increases dramatically in terms of breadth, depth and volume.
    • The largest alpha events cluster, such that many of the “alpha spikes” derive from short positions during the financial crisis when it was difficult or impossible to take these positions.
  • During the nine-month out-of-sample test, a portfolio driven exclusively by the news metadata filter model beats the S&P 500 Index by a net 11.5% (see the chart below).

The following chart, taken from the paper, compares the out-of-sample cumulative trading performance of the news metadata filter model to that of the S&P 500 Index (apparently excluding dividends) during the first three quarters of 2010. During this period, the model generates a positive return and beats the index by 11.5%.

In summary, evidence indicates that traders may be able to beat the stock market by systematically filtering a broad and deep source of news metadata to identify sentiment extremes for individual stocks.

Cautions regarding findings include:

  • The out-of-sample test period is short in terms of variety of market conditions.
  • Tests apparently do not allocate any cost of using the Thomson Reuters News Analytics service to trading friction. This cost would reduce reported returns.
  • Since smaller capitalization stocks apparently drive profitability and test portfolio allocation rules are not be based on market capitalization, the S&P 500 Index may not be an appropriate benchmark. For example, the total return for iShares S&P 1500 Index (ISI) during the first three quarters of 2010 is 4.2%, compared to 3.5% for S&P 500 SPDR (SPY). And, the total return for Rydex S&P 500 Equal Weight (RSP) during the first three quarters of 2010 is 8.1%.
  • The assumed level of trading friction may be optimistic for many traders, especially during market crises or narrower crises specific to traded stocks.
  • Market adaptation to widespread use of news metadata is plausible.
Why not subscribe to our premium content?
It costs less than a single trading commission. Learn more here.
Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts