Streaming Delayed Price  /  Updated: 5:19 PM EST, Nov 26, 2014  /  Add to My Watchlist      
Historical Prices
Date Open High Low Close Volume Change (%)
Nov 26, 2014 0.3500 0.3500 0.3500 0 +0.00(+0.00%)
Nov 25, 2014 0.3700 0.3700 0.3380 0.3500 10,470 -0.02(-5.23%)
Nov 24, 2014 0.3300 0.3693 0.3300 0.3693 28,437 +0.03(+7.98%)
Nov 21, 2014 0.3990 0.4000 0.2820 0.3420 248,744 -0.06(-14.29%)
Nov 20, 2014 0.4002 0.4500 0.3900 0.3990 46,773 -0.05(-10.54%)
Nov 19, 2014 0.4349 0.4460 0.4070 0.4460 29,051 +0.02(+4.23%)
Nov 18, 2014 0.4400 0.4450 0.4000 0.4279 39,035 -0.02(-4.91%)
Nov 17, 2014 0.4400 0.4800 0.4200 0.4500 23,116 -0.00(-0.44%)
Nov 14, 2014 0.4830 0.5100 0.4001 0.4520 32,853 -0.05(-10.32%)
Nov 13, 2014 0.4800 0.5099 0.4800 0.5040 13,664 -0.01(-1.23%)
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AXION INTL HLDGS (AXIH) Historical Stock Prices

Welcome to the AXION INTL HLDGS historical stock prices page. Here you can search stock prices for AXION INTL HLDGS by Month, Year or specify a specific date range. The table is historical stock prices are displayed by Date, Open Price, High Price, Low Price, and Close Price. Use the scroll bar to scroll down the page and click on the Download button in the bottom right hand corner should you wish to download any of the AXION INTL HLDGS Historical Stock Prices.

More Historical Stock Prices for AXION INTL HLDGS

Researchers have found that some of the biggest price deviations from random walks result from seasonal and temporal patterns. The historical stock prices for AXION INTL HLDGS (AXIH) are no exception to this rule. In particular, returns in January significantly exceed those in other months (January effect) and on Mondays stock prices go down more than on any other day. Observers have noted these effects in many different markets for more than half a century, but without succeeding in giving a completely satisfactory explanation for their persistence. Stock Analysisuses most of the anomalies to extract information on future price movements from historical data. The Stocknod stock alertsand stock signalsuse this same concept to identify optimal entry and exit points. But some economists, for example Eugene Fama, argue that most of these patterns occur accidentally, rather than as a result of irrational or inefficient behavior of investors: the huge amount of data available to researchers for analysis allegedly causes the fluctuations.