• Title/Summary/Keyword: target price accuracy

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Do Analyst Practices and Broker Resources Affect Target Price Accuracy? An Empirical Study on Sell Side Research in an Emerging Market

  • Sayed, Samie Ahmed
    • The Journal of Asian Finance, Economics and Business
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    • v.1 no.3
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    • pp.29-36
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    • 2014
  • This paper attempts to measure the impact of non-financial factors including analyst practices and broker resources on performance of sell side research. Results reveal that these non-financial factors have a measurable impact on performance of target price forecasts. Number of pages written by an analyst (surrogate for analyst practice) is significantly and directly linked with target price accuracy indicating a more elaborate analyst produces better target price forecasts. Analyst compensation (surrogate for broker resource) is significantly and inversely linked with target price accuracy. Out performance by analysts working with lower paying firms is possibly associated with motivation to migrate to higher paying broking firms. The study finds that employing more number of analysts per research report has no significant impact on target price accuracy -negative coefficient indicates that team work may not result in better target price forecasts. Though insignificant, long term forecast horizon negatively affects target price accuracy while stock volatility improves target price accuracy.

A Comparative Study between Stock Price Prediction Models Using Sentiment Analysis and Machine Learning Based on SNS and News Articles (SNS와 뉴스기사의 감성분석과 기계학습을 이용한 주가예측 모형 비교 연구)

  • Kim, Dongyoung;Park, Jeawon;Choi, Jaehyun
    • Journal of Information Technology Services
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    • v.13 no.3
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    • pp.221-233
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    • 2014
  • Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

A Two-Phase Hybrid Stock Price Forecasting Model : Cointegration Tests and Artificial Neural Networks (2단계 하이브리드 주가 예측 모델 : 공적분 검정과 인공 신경망)

  • Oh, Yu-Jin;Kim, Yu-Seop
    • The KIPS Transactions:PartB
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    • v.14B no.7
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    • pp.531-540
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    • 2007
  • In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

An Implementation of Optimum Tender Price Automatic Calculation System using Statistical Analysis Technique (통계분석 기법을 이용한 최적의 투찰가 자동 산출 시스템의 구현)

  • Kim, Bong-Hyun;Lee, Se-Hwan;Cho, Dong-Uk
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.11B
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    • pp.1013-1019
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    • 2008
  • Recently, various information and data are efficiently used by the rapid growth of its Internet in our real life. But, users have spent lots of time finding necessary information for the increased amounts of information. To solve this problem, it can be provided the speed, accuracy of information search with development of intelligent search engines, agent system etc. In this paper, we propose the method of getting the best tender price in the analysis of the construction bid information that needs its professionalism by on the purpose to maximize users' satisfaction. Of course, if it is not under the unit of a results in the future, we put target of this paper on part to heighten supreme successful bid success rate. Therefore, this paper embodies offered system of web based on producing tender price of most suitable through techniques to produce tender price about successful bid that compare with bidder fare by statistical analysis incidental and value approaching successful bidder fare by frequency analysis method.

Does the Geography Matter for Analysts' Forecasting Abilities and Stock Price Impacts? (기업 본사 소재지에 따른 애널리스트의 이익 예측능력 및 주가영향력 차이가 존재하는가?)

  • Kim, Dong-Soon;Eum, Seung-Sub
    • The Korean Journal of Financial Management
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    • v.25 no.4
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    • pp.1-24
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    • 2008
  • We empirically examined the forecasting abilities of analysts in the Korean stock market with regard to their earnings estimates, and the impacts of their reports on stock prices. Further, we also examine if there is any difference in analysts' forecasting accuracy and stock prices impacts depending upon the geographical distance between analysts and companies they follow. We found the following interesting empirical results. First, analysts have tendency to overestimate sales, operating income, and net income, consistent with the previous literature. Second, the degree of overestimation depends upon the geography of companies. That is, it is smaller for companies headquartered in Seoul than companies in local provinces. Third, analysts' earnings estimates are also more accurate for companies located in Seoul. So, we conjecture that analysts have easier access to the information for the companies. Fourth, when analysts downgrade target prices, companies in Seoul are less negatively affected than those in local provinces. Even when analysts revise downward stock recommendations, stock prices of companies in Seoul go up. Overall, analysts' price impacts are more favorable for Seoul-located companies. Last, but not least, when foreign ownership is higher, investors react less negatively to downward revisions of stock recommendation, but react more negatively to downward revisions of target prices.

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A Study on Stock Trend Determination in Stock Trend Prediction

  • Lim, Chungsoo
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.12
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    • pp.35-44
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    • 2020
  • In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

Precision evaluation of the treatment that used coordinates confirmation of couch in case of two forgets adjoined. (Couch의 좌표 확인을 이용한 치료 위치 이동의 정확성 평가)

  • Seo Jeong-min;Jeong Cheon-young;Park Young-hwan;Song Ki-won
    • The Journal of Korean Society for Radiation Therapy
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    • v.15 no.1
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    • pp.35-40
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    • 2003
  • I. Purpose Confirming an error to be able to break out in a method to move couch manually while operator sees the skin marks on patient in case of curing head who got 2 targets adjoined, so we analyze coordinates price of couch, evaluate reproducibility and precision of change movements between targets. II. Materials and Methods In radiotherapy, for confirming errors in manual movements by operators by exchanging between two targets to treat patient head, we read coordinates price(vertical, longitudinal, lateral three directions of couch) shown on a monitor of LINAC( CL 2100, Varian, USA) in order to evaluate accuracy about the length that moved in time for moving couch manually. After reading movement length of coordinates recorded in three directions of all treatment, we compared distance between targets recorded in RTP(Pinnacle, ADAC, USA) with reading coordinates price of couch, setting actually done the same patient for ten times, coordinates were recorded, treated for evaluating averages and degrees of errors and standard deviations. III. Results In method to confirm skin marks of patient by operators' view and to move couch manually, average standard deviations of movements between two targets are vertical 1.4mm, longitudinal 0.9mm, lateral 2.2mm in each direction. As for the error in straight dimension, it is about 3.6mm averages and 5.1mm maximum. The average of errors in each directions was vertical 1mm, longitudinal 0.7mm, lateral 2.7mm. The greatest error broke out in lateral direction with $25\%$ of all cases ; to exceed an error average. IV. Conclusions If operators moved manually couch for changing target points, errors about 3.6mm average degrees occur. It is important that operators confirm the errors prices of actual couch coordinates for asking a correct movement between the targets adjoined each other ; in case of treatment demanding high precision like 3D conformal therapy or IMRT. Therefore, if we apply couch coordinates confirmation to reproducibility and to precision evaluation of treatment, it's expected that we can execute high-quality radiotherapy.

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A Prediction Model for Agricultural Products Price with LSTM Network (LSTM 네트워크를 활용한 농산물 가격 예측 모델)

  • Shin, Sungho;Lee, Mikyoung;Song, Sa-kwang
    • The Journal of the Korea Contents Association
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    • v.18 no.11
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    • pp.416-429
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    • 2018
  • Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model's accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.

A Study On Predicting Stock Prices Of Hallyu Content Companies Using Two-Stage k-Means Clustering (2단계 k-평균 군집화를 활용한 한류컨텐츠 기업 주가 예측 연구)

  • Kim, Jeong-Woo
    • Journal of the Korea Convergence Society
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    • v.12 no.7
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    • pp.169-179
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    • 2021
  • This study shows that the two-stage k-means clustering method can improve prediction performance by predicting the stock price, To this end, this study introduces the two-stage k-means clustering algorithm and tests the prediction performance through comparison with various machine learning techniques. It selects the cluster close to the prediction target obtained from the k-means clustering, and reapplies the k-means clustering method to the cluster to search for a cluster closer to the actual value. As a result, the predicted value of this method is shown to be closer to the actual stock price than the predicted values of other machine learning techniques. Furthermore, it shows a relatively stable predicted value despite the use of a relatively small cluster. Accordingly, this method can simultaneously improve the accuracy and stability of prediction, and it can be considered as the new clustering method useful for small data. In the future, developing the two-stage k-means clustering is required for the large-scale data application.

Drone Obstacle Avoidance Algorithm using Camera-based Reinforcement Learning (카메라 기반 강화학습을 이용한 드론 장애물 회피 알고리즘)

  • Jo, Si-hun;Kim, Tae-Young
    • Journal of the Korea Computer Graphics Society
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    • v.27 no.5
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    • pp.63-71
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    • 2021
  • Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.