Recently, Deltix was spotlighted in two industry publications – CTA Intelligence and e-FOREX. Below are synopses of the two articles and links to view the original articles (no subscription or login needed).

DIY Execution Analysis

The CTA Intelligence Special Report: Managed Futures 2016 included an article by Stuart Farr titled “DIY Execution Analysis.” In the article, Stuart talks about the high cost and potentially undesirable characteristics of purchasing historical market data versus recording your own market data. Unless you’re running a start-up firm, it’s better to record real time market data for use in back-testing and execution analysis (or TCA).

The article discusses multiple advantages of recording your own market data. For example:

• You can capture all of the latencies and idiosyncrasies inherent in your own infrastructure and market data feeds. This is most important for strategies or analysis (like execution quality) that require tick and market depth data.
• If you’re doing forensic execution analysis, it is doubly necessary as the time-stamping of orders, executions and market data needs to be fully sequenced. Such forensic execution analysis may not be required on a continuous basis, but is essential when there is an unexpected or unexplained change in execution quality.
• For orders executed algorithmically, ongoing analysis is useful to track performance of the algos. Analysis can provide comfort or alert to unacceptable changes in execution quality measured in both ticks and dollar value. Time-series analysis of executions can be used to look for patterns of over/under performance and improve execution algo selection and parameterization.
• A practical key to success is to demonstrably and frequently improve trading performance by fine-tuning execution. There is nothing like showing real dollar improvement to keep researchers, technologists and traders motivated and focused.

Stuart goes into detail about each of these points. You can access the article here (no registration or login required).

Strategy Back Testing Platforms

e-FOREX published an article, “Sharpening the tools of the trade: New Developments with FX Strategy Back Testing Platforms”, that features comments from Ilya Gorelik on the topic of back testing foreign exchange (FX) trading strategies. This article talks about the challenges in signal generation and back testing in FX and how new technologies (e.g. cloud) can assist.

The subject of the e-FOREX article is back-testing only. As Deltix clients already appreciate, it is extremely important to be able to deploy a backtested trading strategy “as is” with no re-coding. This helps to protect the fidelity of actual results versus back-tested results. It also reduces time-to-market and the overall cost of the strategy development and deployment process. Deltix is the only institutional grade platform that provides this full, research-to-live-deployment capability.

You can read this article here (no registration or login required).

The Deltix Product Suite provides end-to-end support of all phases of the alpha discovery process, including data collection and aggregation, model development, back-testing, simulation and deployment to production. Learn more here.

Introduction

Company earnings are the bedrock of financial analysis and investment. Sell-side, buy-side and independent research analysts perform quantitative and qualitative analysis of companies, their peers and their markets in order to provide guidance for short-term earnings and earnings growth for in-house use or for clients. Innovations in earnings analysis over the last few years have included crowd-sourced earnings estimates (e.g. Estimize) and sentiment derived from the news and social media (e.g. Ravenpack, Social Market Analytics, PsychSignal). The overriding objective of company analysis has been and still is to forecast as accurately as possible a company’s future earnings and so guide asset allocation and trading decisions.

As a software and services firm focused on quantitative research and trading solutions, we are always interested in alternative ways to apply quantitative techniques for trading and investment purposes. This fall, we decided to look at company earnings from a different perspective. This time, we looked at how  calendar events affect forecasts: specifically, we wanted to test whether earnings announcement date revisions can be used for predicting future prices in a manner that could be profitably traded upon.

We reviewed the recent research papers of Joshua Livnat (http://www.wallstreethorizon.com/livnat) and Eric So (http://www.wallstreethorizon.com/So), both of whom look at whether changes in the earnings announcement dates can be used to generate returns.

Research Methodology

We conducted our study using our own research software, TimeBase and QuantOffice. Below are the steps we followed for this research:

1. We built a data loader to populate TimeBase with the WSH daily snapshots of company future earnings announcement dates for S&P500 stocks for the period January 3, 2006 to September 2, 2015.
2. We also populated TimeBase with market data for those stocks for the same period. In an actual production deployment of Deltix software, a time series of tick data is automatically recorded through operation of the software for trading. For our research study, we back-populated TimeBase with one-minute bar data.
3. We now had a base data set on which to apply and test our ideas. Quant researchers use Deltix to express their model ideas as “strategies” in QuantOffice. In the studies by Joshua Livnat and Eric So referenced above, both found that companies who advance their earnings dates generally outperform companies that delay their earnings dates. It is not difficult to rationalize why this might hold true and so we started with this premise and then developed the theme with advanced statistical techniques implemented in QuantOffice.
4. The resulting model was back-tested as a trading strategy. (In practice, there were of course multiple iterations of the model with each backtest providing our researchers feedback for the next iteration. As each back-test takes seconds to run: the productivity of this iterative approach is very high).
5. Where both earlier studies modelled a holding period spanning from shortly after the change in date to the actual announcement, we took positions the day before an earnings announcement and sold them the day after, resulting on a holding period of less than 24 hours.
6. In order to isolate the calendar date effect from any general market effect (although our holding period was less than a day), we also implemented a dollar-neutral version of the strategy.

Results

Our results supported the findings of the previous researchers. Specifically, we found:

• The most likely positive returns occured when the earnings announcement date was advanced (i.e. brought forward) in the second half of the quarter.
• Conversely, the most probable negative returns occurred when earnings announcement date was delayed in the first half of the quarter.
• For both hedged and un-hedged versions of the strategy, for the period January 2006 to September 2015, the back-tested strategies showed Sharpe Ratios of 2.08 (unhedged) and 2.12 (hedged) with average profit per share of 10 cents and 8 cents respectively.

As such, we can conclude that there are profitable opportunities from trading with signals derived from WSH earnings date announcement data.

The P&L curve for the unhedged version of the strategy is shown below:

 

The full results are included in our research paper.

 

Practical Applications – Research

One of the impediments to firms seeking alpha in alternative data sources is the time required to get to the point where consideration of the value of such sources can even start. Data is usually delivered as a real-time time stream or, for sample or trial purposes, a flat file. In either case, the raw data has to be populated into an analytical platform duly configured for the data.

By building a set of data loaders (real-time and batch) for our TimeBase data repository, Deltix significantly reduces the time required for setup before analysts can start their research. In addition, QuantOffice has a rich set of analytical libraries which enable quant researchers to focus on implementing their specific logic, rather than having to spend time on the time-series analytics required for such an implementation.

The Deltix Quantitative Research Team invested substantial time to prepare the data loaders and repository for this research study, and it is now available in the Deltix Product Suite. Researchers can access a paid trial in which they can access the data and QuantOffice strategy and modify it for their own purposes.

Practical Applications – Trading

Both Livnat and So generated long and short positions from soon after the date of the revision to just after the actual earnings announcement. Both found that positive returns can be generated from taking long and short positions, but both also found that the majority of returns generated from taking positions in advance of the actual earnings occurred on or around the actual EPS announcement. The strategy suggested in our research therefore focused on taking positions just before to just after the earnings announcement (holding period less of than 24 hours). This short-term holding period suggests that institutional managers could implement tactical trading around core positions in a portfolio in addition to outright portfolio construction.

To review the detailed research study, click here.

 

To get more information about the products used to conduct this research, follow the links below:

TimeBase
QuantOffice
QuantServer

 

To learn more about the paid trial that provides access to the strategies and data used in this research, click here.

Corporate Reputation Data

The Corporate Reputation Index (CRI), developed by Seldonix and distributed in partnership with Social Market Analytics, allows traders to identify and compare over- and under-priced stocks that are affected by a company’s actions. Corporate Reputation is the controllable portion of a company’s stock price, considered to be an intrinsic driver of company performance, and a major contributor to daily stock price fluctuation.

The Corporate Reputation Index is the first daily business metric for monitoring corporate reputation. Centered on “0”, companies with negative values are deemed undervalued relative to market while those with positive values are overvalued.

The CRI is created after the close of markets and is currently available before markets open the next day at any time after 9:00 PM Eastern US Time the night before.

Each data record features a set of fields:

• The timestamp associated with each field is the market close date of the data used;
• Symbol;
• CRI

Basis of Research using Seldonix data

The purpose of the research described in this paper is to determine if there are opportunities to generate alpha in US equities traded on the NYSE and NASDAQ using Seldonix data as a basis for daily market movement prediction (the holding period is about 6.5 hours).

We show how with the use of simple technical indicators and the popular conception of basket trading, we can exploit the CRI to generate excess returns. An evaluation of the CRI was conducted in the forecasting of stock price. The results of the testing showed a 1 – 8 day lag in the prediction of stock price, depending on the stock. Therefore our purpose is to determine the most appropriate time when the CRI generates the most suitable values for opening a position. As the CRI values change every day and are different for each ticker, we need to construct an approach which will successfully encompass both equally important collations. To identify these situations, we need to:

• Compare short-term values to long-term;
• Compare values within the considered universe of tickers.

The full paper can be accessed here.

Results

This approach gives the following results for S&P 100 tested on the period from 1/5/2010 to 8/18/2014:

Parameter	All Trades	Long Trades	Short Trades
Net Profit/Loss	21,182.82	13,984.97	7,197.85
Total Profit	113,708.71	59,129.80	54,578.91
Total Loss	-92,525.89	-45,144.83	-47,381.06
Cumulated Profit %	21.18 %	13.98 %	7.20 %
Max Drawdown	-2,590.71	-2,774.62	-2,265.55
Max Drawdown %	-2.41 %	-2.66 %	-2.18 %
Max Drawdown Date	9/2/2011	9/30/2011	9/15/2011
Return/Drawdown Ratio	8.18	5.04	3.18
Drawdown Days %	80.96 %	80.36 %	88.29 %
Max Drawdown Duration	119	198	188
CAGR	4.40 %	2.97 %	1.57 %
Sharpe Ratio	1.73	1.37	0.73
Annualized Volatility	2.54	2.16	2.15
Sortino Ratio	2.86	2.15	1.13
UPI	0.39	0.27	0.13
Information Ratio	1.71	1.37	0.72
Optimal f	68.32	63.51	33.96
All Trades #	2308	1166	1142
Profitable Trades Ratio	0.53	0.54	0.52
Winning Trades #	1229	634	595
Losing Trades #	1079	532	547
Average Trade	9.18	11.99	6.30
Average Winning Trade	92.52	93.26	91.73
Average Losing Trade	-85.75	-84.86	-86.62
Avg. Win/ Avg. Loss Ratio	1.08	1.10	1.06
Average Profit per Share	0.04	0.05	0.03
Max Consequent Winners	9	11	9
Max Consequent Losers	10	10	9

 

Rules for closing

The other idea is to exploit the CR trend not only for opening but for closing position. On the figure below you can see the example of position on AAPL chart, blue line on second pad indicates CRI values:

We use the Simple Moving Average (SMA) of the last CRI values (red line on the second pad) to determine whether the instrument gets over or underpriced. Using the model from the paragraph Over/Under Priced, we will close the position for the instrument according to following rule:

1. If CR > SMA (5, CR) (instrument is overpriced at time t, price is likely to return to suggested price at t+1) close Short position on day close;
2. If CR < SMA (5, CR) (instrument is underpriced at time t, price is likely to return to suggested price at t+1) close Long position on day close.

The same chart with this rule shows better results:

This approach gives the following results for S&P100 tested on the period from 1/5/2010 to 8/18/2014:

Parameter	All Trades	Long Trades	Short Trades
Net Profit/Loss	32,118.49	18,265.41	13,853.08
Total Profit	143,984.75	71,784.79	72,199.96
Total Loss	-111,866.26	-53,519.38	-58,346.88
Cumulated Profit %	32.12 %	18.27 %	13.85 %
Max Drawdown	-3,526.80	-4,508.75	-3,368.95
Max Drawdown %	-3.08 %	-4.22 %	-3.07 %
Max Drawdown Date	9/2/2011	9/30/2011	9/15/2011
Return/Drawdown Ratio	9.11	4.05	4.11
Drawdown Days %	80.45 %	81.83 %	88.63 %
Max Drawdown Duration	95	194	279
CAGR	6.44 %	3.83 %	2.95 %
Sharpe Ratio	1.97	1.31	0.94
Annualized Volatility	3.27	2.93	3.14
Sortino Ratio	3.40	2.06	1.55
UPI	0.58	0.26	0.19
Information Ratio	1.90	1.31	0.91
Optimal f	60.05	44.63	29.93
All Trades #	2190	1103	1087
Profitable Trades Ratio	0.53	0.55	0.51
Winning Trades #	1158	603	555
Losing Trades #	1032	500	532
Average Trade	14.67	16.56	12.74
Average Winning Trade	124.34	119.05	130.09
Average Losing Trade	-108.40	-107.04	-109.67
Avg. Win/ Avg. Loss Ratio	1.15	1.11	1.19
Average Profit per Share	0.06	0.07	0.05
Max Consequent Winners	13	16	16
Max Consequent Losers	11	9	15

 

Filter for stronger signal

One more idea is to filter a stronger signal when CR retains its sign from the previous day, which is a stronger signal indicating overpriced/ underpriced asset. On the chart below you can see two trades on MSFT asset, the second one is unprofitable, when the CR line changes its value from -1 to 1:

Filtering out such unstable situations gets the following results for the S&P 100 tested in the period from 1/5/2010 to 8/18/2014:

Parameter	All Trades	Long Trades	Short Trades
Net Profit/Loss	32,810.12	19,145.45	13,664.67
Total Profit	126,322.99	64,003.72	62,319.27
Total Loss	-93,512.87	-44,858.26	-48,654.61
Cumulated Profit %	32.81 %	19.15 %	13.66 %
Max Drawdown	-2,617.49	-4,409.37	-2,465.16
Max Drawdown %	-2.37 %	-4.18 %	-2.29 %
Max Drawdown Date	9/2/2011	9/30/2011	9/15/2011
Return/Drawdown Ratio	12.53	4.34	5.54
Drawdown Days %	78.38 %	80.02 %	86.74 %
Max Drawdown Duration	89	194	176
CAGR	6.56 %	4.00 %	2.91 %
Sharpe Ratio	2.16	1.49	1.03
Annualized Volatility	3.04	2.68	2.84
Sortino Ratio	3.82	2.45	1.70
UPI	0.67	0.26	0.24
Information Ratio	2.11	1.50	1.01
Optimal f	70.80	55.51	36.20
All Trades #	1839	937	902
Profitable Trades Ratio	0.53	0.55	0.51
Winning Trades #	972	512	460
Losing Trades #	867	425	442
Average Trade	17.84	20.43	15.15
Average Winning Trade	129.96	125.01	135.48
Average Losing Trade	-107.86	-105.55	-110.08
Avg. Win/ Avg. Loss Ratio	1.20	1.18	1.23
Average Profit per Share	0.07	0.08	0.06
Max Consequent Winners	12	12	11
Max Consequent Losers	11	10	16

 

Conclusion

We presented an approach that uses CR indices as a strong predictive factor of price directionality. We developed a trading strategy that implements an algorithm based on this approach. We also constructed two other versions, both based on CRI predictive power, that raise the performance of the main approach.

The main contribution of the paper is back-testing and comparison of the different versions of the strategy. On stocks in the S&P 100, back-testing shows that the first strategy has an average Information Ratio of 1.71 over the period 2010-2014, the second and third versions achieved Information Ratios of 1.90 and 2.11 respectively, which indicates that the CRI is predictive in the forecasting of stock returns.