Questions tagged [prediction]

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Machine learning in stock price prediction [duplicate]

I am new and thinking to experiment in the stock price predication. There are many way like moving average but I am interested in using machine learning. Anyone can help me here to give pointer?
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1answer
76 views

Book/Material recommendation - Stock Price Forecasting using AI tools

I am looking for a book, which covers the following topics: stock price prediction using Artificial Neural Network, stock price prediction using LSTM, stock price prediction using linear/non-linear ...
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79 views

stock price trend classification using Random Forest in sklearn

I have created a random forest classification model in skicit-learn, but I am unsure how to finalise my forecast. I have built the model and it is showing good results on the testing data. I get a ...
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1answer
83 views

Prediciting outperformance - choice of statistical design?

I want to predict relative outperformance between a stock and an associated benchmark index using statistical time-series models (e.g. ARIMA) and some exogenous variables (day of the week, corporate ...
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150 views

Current research in price prediction

I am returning to studying the markets after ten years spent in banking. I would like to ask for directions, what approaches to price prediction are currently used - I want to catch up quickly. Papers ...
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1answer
43 views

Find best linear predictor of $X_2$ given $1, X_1$

I'm having a problem calculating the best linear predictor of a time series. I'm using the book Brockwell-Davis 2016 - Introduction to Time Series and Forecasts. First let me write down one notational ...
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49 views

Why is the approximate entropy of (some) stock returns zero?

I downloaded some prices for TSLA and AMZN from yahoo finance to try and see if I could measure the entropy on a rolling basis with the intention being maybe returns have lower entropy (are more ...
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40 views

Predicting smoothed returns

Due to the extremly low ratio of signal to noise in financial data, predicting raw returns is very difficult. If we smooth out the price time series, say by an EWMA, and then calculate returns on this ...
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56 views

Normalization of cryptocurrency / stock price data

I am making predictions on cryptocurrency / stock price data. I am using the candlestick data (High, Low, Open, Close) to estimate the close price of the next day. I am looking for the right way to ...
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1answer
49 views

Non-fixed stationary “conversion”

Dear users of StackExchange, I was wondering why the log returns of a fixed period of time is such a common use in "transforming" a time series into a more stationary one? I thought that ...
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1answer
82 views

Using candlesticks for Stock price direction prediction

I am working on a college project wherein I want my machine learning model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as ...
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50 views

How to use machine learning to generate optimal allocations for an instrument?

What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available ...
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48 views

Why are prediction markets based on logarithms when a linear solution can suffice?

For example, take a binary outcome; A coin toss, heads or tails. If heads, then those that picked heads receive \$1 and tails receive \$0. To quote the prices for each bet Hanson's LMSR uses ...
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23 views

Is it possible to compare Forex data to similar random time series to measure how predictable it is?

In relation to my previous question (Who influences Forex prices and by how much?) I have an raw idea how to determine how much is Forex influenced externally and how much is its behavior given by its ...
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57 views

Please help me understand this dataset regarding stock prices

I am supposed to predict column E but I cannot figure out what any of these columns mean. The information provided with the dataset is as follows: column A: past 28 week slope value column B: past 48 ...
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1answer
107 views

Feature engineering for mid-price prediction - quickly changing features

I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
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3answers
223 views

Consistent offset/lag in time-series prediction using Neural Network (all code provided)

I'm using a neural network (keras package) to predict Bitcoin prices 48 hours in advance. The issue is that for some reason, my predictions are "correct" but they are lagging behind the true ...
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1answer
137 views

Reintegrating Fractionally Differentiated Time Series Prediction

I am working on a supervised learning approach to Time Series Regression, and am currently investigating fractionall differentiation (optimizing the stationarity/information tradeoff) discussed ...
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66 views

Garch models - are they useful for hedging? If so how?

I understand that Garch models are useful to predict volatility. But are they useful for hedging in practice? If I want to hedge volatility, why shouldn't I just use a Variance Swap? In other words, ...
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1answer
137 views

Predict Log Stock Return Direction and Trading Strategy

The $k$ period log return is defined as $$r_{t}(k)=log(S_{t}/S_{t-k}),$$ Where $S_{t}$ is the stock closing price at time $t$. For argument sake, assume that by time I mean a stock trading day and ...
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1answer
76 views

Is option surface same as future price probability surface?

Let's consider the Option Chain for the Stock. There are two 3D surfaces representing the probability of the future stock price and the option prices. I wonder if they are representing the same thing? ...
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22 views

Tradeoffs of using Loess regression to fit random walks

I am curious if anyone has had much experience attempting to predict random walks using Loess regression or a variant of local statistical methods.
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3answers
120 views

Does asset volume, rather than asset returns, predict performance?

Asset returns are the most common data type used in finance. They are derived from closing price data. Ordinary level 1 data for stocks not only consists of closing prices, but also gross volume ...
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1answer
38 views

Mutual fund rating predictions

I am working on a dataset with aim to predict the MF ratings. There are cols like, 10 yr, 7 yr, 5 yr etc returns. I also have commencement date of MFs, the question is there are MFs with commencements ...
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1answer
70 views

How to use multi-periods and mult-factors to predict stock price by linear regression?

Give data in $t_n$ denoted by $[x_1^n, x_2^n, ... x_d^n]$ and label $y_n$ to be predicted. We can just train a $d$-dimensional linear regression $y_n=\sum b_ix_i^n$ to make a prediction. However, I ...
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1answer
68 views

If a security has many options expiring on a day, can you predict its price direction?

r/wallstreetbets post alleges that because ~$1.9 Trillion USD of SPY call options expire on 3/20, the price of SPY will skyrocket on 3/20. Is this correct? What can be deduced? I'm not expecting to ...
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1answer
76 views

Predicting time series based on another

This is more of a generic question, but I'm sure it has a best answer/methodology which is what I'm trying to reach. I'm trying to figure out a solid line of thought when looking at a time series X ...
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1answer
53 views

Relationship between Data Size and Arima Prediction Interval Width?

When we use Arima model to acquire Interval Predictions, will the width of prediction intervals decrease if we use more data (longer history) to fit the model?
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2answers
46 views

Updated Time Series Prediction Model When acquiring new data Points - Basic Question

Suppose I have a Time Series Model (assume ARIMA) and use it to make one-step ahead prediction. If I acquire a new data point, (for example I was originally using the first 100 days to fit an Arima ...
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68 views

Replication of the paper: “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction”

I recently replicated the paper "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction" and found out that my estimation of the equity premium differs from the data provided ...
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1answer
251 views

Question is about the data in the paper: “A Comprehensive Look at The Empirical Performance of Equity Premium Prediction”

I would like to ask a question if you download the data from the Amit Goyal website: http://www.hec.unil.ch/agoyal/ You will see that there are two columns "CRSP_SPvw" "CRSP_SPvwx", they are ...
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1answer
176 views

Proper Data Partitioning For Building a Forecasting Model

Goal: A team and I are looking to build a model that performs a predictive action for the state of the market on day T + n, using the data at hand on day ...
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111 views

How to weigh computational cost of updating an online predictive model for latency-constrained trading (e.g., market making, HFT)?

Say one has a predictive online model for market making or HFT (or just for anything strictly latency-constrained). In my specific example, I start with a Gaussian distribution over the "true value" ...
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1answer
154 views

time series data modeling for deep learning

what is the best format to feed the input data, which are time series with varying density over time, to a deep learning network, while at any iteration we want to feed a batch of data including a ...
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28 views

Which technique determines if var x1 leads var y? Assuming var x1 may need to be transformed

Suppose I want to predict future changes in variable y (stock price over time). I notice that variable x1, inverted and delayed three months, tends to lead y. Which technique can I use to find other ...
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404 views

Beginner FFT (Fourier) transforms on closing prices for Apple

I don't know math very well, but I have been programming for many years. I would like to use FFT as a parameter to a ML model. The FFT is diving down sharply. I tried many stocks and its the same. ...
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47 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. ...
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104 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 ...
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66 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 ...
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4answers
318 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 ...
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91 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 ...
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70 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 ...
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2answers
282 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 ...
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1answer
189 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 ...
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2answers
481 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 ...
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230 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 ...
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59 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 ...
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294 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 ...
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2answers
393 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, ...
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1answer
251 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 ...