Questions tagged [prediction]
The prediction tag has no usage guidance.
65
questions
0
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0answers
23 views
Labeling Returns in 5 categories based on BL view approach
I have to label a time series of returns into 5 categories based on the Black Litterman view approach.
The categories should look as follows:
very bullish: + 2 std. dev.
bullish: + 1 std. dev.
...
2
votes
0answers
25 views
Mean directional accuracy and zero
I'm trying to use mean directional accuracy to evaluate my directional predictions in back-test, but it can't deal with realised directions which are 0, due to the comparison of the signs of ...
1
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0answers
49 views
Predicting stock returns using principal components of macroeconomic variables
I'm trying to detect return predictability by regressing stock returns on the first couple of principal components of a set of macroeconomic variables. I'm doing this for different stock styles such ...
3
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4answers
155 views
Predicting portfolio returns
I suppose there are roughly two approaches to predict portfolio returns.
Either predict the returns of all underlying stocks and aggregate all individual stock predictions, or predict the portfolio ...
0
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0answers
64 views
Can I make the simple moving average less lagging by this method?
I have (T+3) predicted prices for a stock. Let us assume that the predicted prices are going to be very close to the actual prices. can I alter the formula of simple moving average for 20 days like ...
3
votes
0answers
65 views
Why can the t-bill rate forecast stock returns?
The tbill rate is used as a predictor of the equity premium in a number of papers.
Whilst there is not a general consensus about whether it is a significant predictor, it is still widely used.
I ...
3
votes
2answers
83 views
Any research on label/target variable design for ML training?
is there any discussion or paper about how to define/design the labels for the ML training? Intuitively I can think of:
Net return of the next future day
Net return using the max candle-high value of ...
1
vote
1answer
164 views
Should there be a relation between stocks when used as input data for integrating Technical Analysis with Machine Learning?
I'm integrating Technical Analysis with Deep Learning for the first phase of my research. I wanted to know how should I pick (or group) stocks as input data and whether there should be relation ...
6
votes
2answers
269 views
Does predictability in a VAR process imply mean reversion or momentum?
There seems to be some disagreement in the literature about this. Define predicability of a stationary series to be $\sigma^2_{t-1} / \sigma^2_t$
Finding mean reverting portfolios using canonical ...
4
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0answers
163 views
Implementing Hanson`s LMSR with Limit Orderbooks
I am trying to integrate Hanson's LMSR (see (see logarithmic market scoring rule)into an order-book with traditional bid/ask-limit orders (in KDB+/Q).
The following functions define the basic LMSR ...
0
votes
0answers
50 views
Trading Signals with Different Lags
I have a momentum signal that gives best predictive power at 3 months and a valuation signal that gives best predictive power at one year. If I combine for a 3 month horizon by "interpolating" the ...
8
votes
1answer
190 views
Are returns predictable, Campbell and Shiller (1988)
Following from the thread,
Drivers of equity returns: dividend yield, change in P/E and dividend (or earnings) growth
1) Why are returns predictable from this, is there a reason?
2) Can we expect ...
1
vote
2answers
184 views
Working with time series with different resolutions
I’m looking at making predictions of close prices (stocks/commodities), and have access to various data sources to help predict.
However, most of these sources are in a different time frame, ...
5
votes
1answer
167 views
how are financial data with sparse and asynchronous features imputed in predictive modeling?
I watched a presentation from a large quantitative finance firm that spends a lot of effort around predictive modeling. One of the points the presenter emphasized was that they deal with a lot of ...
1
vote
2answers
136 views
Fractal market hypothesis testing
I would like to do an analysis on the AEX stock exchange index for the last 20 years, but I ran into some issues. It would be really appreciated if you can answer my questions:
In order to apply ...
4
votes
1answer
277 views
What is the stambaugh bias? Why is it important for predictability regressions?
What is the Stambaugh bias? Why is it important for predictability regressions?
Can anyone explain it in simple terms?
4
votes
2answers
766 views
What is time-varying risk premium? Forecasting stock returns
I am trying to understand the concept 'Time-varying aggregate risk premium'.
Here is an extract from a Forecasting book, written by Rapach and Zhou,
"However, rational asset pricing theory posits ...
0
votes
1answer
138 views
Momentum Analysis on Indices
I'm interested in analysis of day-on-day momentum of certain large indices. In particular, I'm interested in the predictive power of the sign of the price change of the first hour of trading with ...
1
vote
1answer
297 views
Predict the behavior of a time series (P&L trading desk)
I work at the trading desk P&L department at a large bank.
The trading desk has positions in almost all sorts of derivatives (options, futures) over a long list of stocks, currencies, commodities.....
4
votes
1answer
294 views
Predict probability of returns: How does changing volatility affect the return pdf?
I am trying to predict the future probability of stock returns based on the return distribution. Therefore I calculate the returns as $\frac{P(t)}{P(t-1)}$ for the whole daily data and fit a ...
9
votes
3answers
2k views
Determine trends of data (direction detection or turning point detection)
I'm working on a model to determine trends (direction detection or turning point detection). Suppose that we have a stock trend which is illustrated below. Blue line is real trend of stock close ...
1
vote
0answers
315 views
R squared statistic in predictions of returns
My question is related to an article which use predictive linear regression for the stock returns. There is told that R squared statistic of 1.6% is high. How can we measure which R squared is high? I ...
2
votes
0answers
250 views
Good books on predictive modeling (for alpha signal research)
In terms of books on predictive models, I find ESL (elements of statistical learning) trying to cover too much and serves more like a reference, instead of explaining and developing the theories for ...
0
votes
1answer
383 views
Trouble understanding lookahead bias
I understand lookahead bias is pretty common industry knowledge. But I cannot wrap my head around how I am introducing it and could use a nice and easy explanation. Here's my thought process.
I have $...
2
votes
4answers
397 views
What is currently predictable in the stock and bond markets and what is not
Disclaimer: I have some knowledge of statistics, machine learning and probability theory, but next to zero knowledge of finance (I had to look up Wikipedia to refresh my knowledge of the difference ...
5
votes
1answer
298 views
Interpret predictions weekly and monthly stock price returns [closed]
I have built a model in R that predicts weekly and monthly returns of stock prices using regression trees, roughly based on https://www.r-bloggers.com/using-cart-for-stock-market-forecasting/. In my ...
2
votes
3answers
187 views
What is the better representative of a P-B ratio for a sector?
What is a better representative of a P-B ratio for a sector, for using it as a factor to predict future returns on that sector? The market weighted average of P-B for all names in that index, or ...
0
votes
2answers
85 views
What kind of indicators would you look in the market preceding a recession/crisis?
What kind of indicators may have predicted the upcoming financial crisis in the 2000 or 2008?
1
vote
1answer
97 views
Window length for predictive regressions
I am building a trading strategy that predicts the current period returns using historical returns (think e.g. using an estimated OLS model to predict next weeks return based on this weeks return). ...
1
vote
2answers
104 views
How to assign n day target variables in machine learning
I am trying to forecast future price using supervised machine learning. My logic is to take open and close price from t, t-1, t-2 and t-3 period to predict future close price in the period t+1,t+3 ...
4
votes
1answer
937 views
How quants use ML models for stock market prediction
I am learning machine learning to use it for stock market price forecasting. While doing that I got this question. If we take any country with stock exchange they have more than one investment assests ...
1
vote
1answer
285 views
Design models using adjusted or unadjusted stock prices (time series prediction)?
I'm creating a predictive model for closing price of stocks (using neural network and support vector machines.). Is it appropriate to use adjusted prices or unadjusted prices for this prediction ...
10
votes
1answer
2k views
ARMA+GARCH prediction with package rugarch (R)
I am analyzing FTSE 100 series, from 2007-01-01 to 2010-12-31 (university exam homework).
I have to use the data 'til 2010-11-30 as sample, and the remaining (23) observations as in-sample forecast (...
1
vote
1answer
54 views
Methods or models to predict activity of clients of a bank
I'm a Physicist but I'd like to know if there are some methods or models to predict the activity of the clients of a bank. I heard that banks are interested in this sort of analysis so I got curious ...
1
vote
1answer
199 views
Building predictive model for closing price using only previous days data
I am trying to determine which quantitative model to try and build a predictive model for the next day's closing price for all the S&P stocks based on their bar for that particular day. However, I ...
2
votes
1answer
995 views
predict next day's close price using hmm
I am reading this paper(Stock market forecasting using hidden Markov model: a new approach) and get confused about how they predict the next day's close price. Below is what the authors say about how ...
0
votes
1answer
53 views
Modeling EOD ETFs price returns together or individually?
Let's say you want to model the next day price returns for a set of US equities large cap ETFs (a relatively homogenous group). Would you model all the ETFs as a single, 15 years data set, or each ETF ...
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0answers
232 views
Estimating the next tick movement in Chinese markets
I'm working on high frequency trading in the Chinese Futures market and I've been having a bit of trouble with getting orders to go through due to the lack of liquidity and large fluctuations. To ...
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vote
3answers
232 views
Technical Indicators reference
I have been looking for a good reference where I can find how technical indicators of stock market analysis are calculated. I have a dataset (time series) which I want to extract these indicators to ...
5
votes
1answer
531 views
What machine learning method is more suitable for prediction of financial time series?
I have time series data for various assets and which I transform to create various features. I have framed the problem as a classification task where I attempt to predict either a positive or negative ...
5
votes
2answers
983 views
Predict Futures Prices based on weather + agricultural data
I’m working in the area of Data Mining and have come up with the following idea for my Masters project.The text may not be the best structured but it’s a working draft to give you a quick idea.
...
0
votes
2answers
862 views
Explanatory variables for regression predicting weekly stock returns
In an empirical analysis I'm trying to predict log() weekly stock returns. I'm trying to model stock returns in a panel data model framework.
As explanatory variables I have 1) a measure of investor ...
0
votes
2answers
697 views
Calculation of price momentum using weekly price observations
In an empirical analysis I'm trying to predict stock returns using different firm characteristics. I would like to use price momentum as an explanatory variable, but I'm not quite sure how to ...
2
votes
4answers
951 views
Implementing A 50/50 Prediction Model Strategy
Reworded the question for clarity (see edits for original post):
How can one knowingly foresee where a 50/50 prediction model will be profitable? For previous posts: I understand that if I have a 50/...
7
votes
2answers
438 views
Quant teams predicting the World Cup
It is a good tradition of the quant teams of the major banks to predict the World Cup. As an example see this new paper from Goldman Sachs:
The World Cup and Economics 2014 (Brazil will win by the ...
11
votes
2answers
16k views
GARCH model and prediction
I have a question about the prediction of volatility and returns of a time series. Basically it is a question about predict in the ...
2
votes
1answer
178 views
Economic indicators leading the yield curve
There is a lot of research on how the government yield curve can be used to predict the economy. The government yield curve is often seen as a leading indicator. But for which variables is the curve a ...
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0answers
88 views
How to choose a window for curve fitting and prediction?
I am using Pareto distribution to fit a serie of survival rates (with least square).
My ultimate goal is to use this fitting curve for prediction. Thus I would mainly focus on the tail of the ...
3
votes
0answers
277 views
Good criteria to sort state-space $\beta_{t}$ according to Kalman filter output
Let's assume the usual state-space linear model without constant term for simplicity:
$y_{t}=\beta_{t} X_{t}+\epsilon_{t}$
If we apply Gaussian Kalman filter to estimate $\beta_{t}$ we get $P_{t}$, ...
12
votes
1answer
2k views
Tools/R code for predicting Dragon-Kings
The theory of the so called Dragon-Kings, esp. by Didier Sornette (ETH Zürich), basically states that financial crises and crashes are predictable (contrary to the theory of black swans).
The ...
